Note: Descriptions are shown in the official language in which they were submitted.
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METHODS AND APPARATUS FOR MULTI-MODAL PREDICTION USING A
TRAINED STATISTICAL MODEL
Cross-Reference to Related Applications
[0001] This application claims priority under 35 U.S.C. 119(e) to U.S.
Provisional Patent
Application 62/678,083, filed May 30, 2018, and titled, "METHODS AND APPARATUS
FOR
MULTI-MODAL PREDICTION USING A TRAINED STATISTICAL MODEL," and to U.S.
Provisional Patent Application 62/678,094, filed May 30, 2018, and titled,
"METHODS AND
APPARATUS FOR MAKING BIOLOGICAL PREDICTIONS USING A TRAINED MULTI-
MODAL STATISTICAL MODEL," the entire contents of each of which is incorporated
by
reference herein.
Background
[0002] The ability to repurpose safe drugs offers great advantages to the
pharmaceutical
industry, including time and cost savings, and increased rate of drug approval
success. The
implementation of computational algorithms aiming to predict new disease
indications for
existing drugs or new treatments for existing diseases have recently emerged
with the
improvements in computer infrastructure and the advent of high throughput
technologies
enabling the characterization of diseases and drugs at a high resolution.
[0003] Some conventional techniques for discovering new disease indications
for existing
drugs or aiming to find the best drug match for a given disease or patient
rely on the genomic
characterization of diseases and the molecular characterization of drug's
mechanism of action in
order to make new predictions. These techniques can be classified as drug-
based or disease-
based, and although both present unique advantages and challenges, a
successful computational
approach usually combines aspects from both techniques.
[0004] Drug-based techniques typically focus on drug structure
similarities, drug molecular
activity similarity or target pathway similarity, and molecular docking. They
use different
information or data modalities, such as drug structures, drug targets, drug
class, and gene
expression perturbation upon drug treatment. Disease-based techniques
typically focus on
associative indication transfer, shared molecular pathology, or side effects
similarities. They
include information or data modalities related to disease-associated mutations
and pathways, and
disease-associated changes in gene expression, or proteins, or metabolites, or
microbiome.
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Examples of approaches combining both drug-based and disease-based rationales
include:
transcription signature complementarity and drug target-disease pathway
similarity.
Summary
[0005] According to one aspect of the technology described herein, some
embodiments are
directed to a method for training a statistical model configured to represent
inter-modality
associations between data in a heterogeneous network. The method comprises
accessing
training data including training data for a first modality and training data
for a second modality
different from the first modality, training the statistical model, the
statistical model comprising
first and second encoders, first and second decoders, and a joint-modality
representation
coupling the first and second encoders to the first and second decoders. The
training comprises
estimating values for parameters of the first and second encoders and the
first and second
decoders using a self-supervised learning technique, at least some of the
training data, and
information describing at least one link between data pairs in the training
data, and storing
information specifying the statistical model at least in part by storing the
estimated values for
parameters of the first and second encoders and the first and second decoders
of the statistical
model.
[0006] According to another aspect of the technology described herein, some
embodiments
are directed to a method for predicting an association between input data in a
first modality and
data in a second modality using a statistical model trained to represent links
between data having
a plurality of modalities including the first modality and the second
modality, the statistical
model comprising a plurality of encoders and decoders, each of which is
trained to process data
for one of the plurality of modalities, and a joint-modality representation
coupling the plurality
of encoders and decoders. The method comprises selecting, based on the first
modality and the
second modality, an encoder/decoder pair or a pair of encoders, from among the
plurality of
encoders and decoders, and processing the input data with the joint-modality
representation and
the selected encoder/decoder pair or pair of encoders to predict the
association between the input
data and the data in the second modality.
[0007] According to another aspect of the technology described herein, some
embodiments
are directed to a method for predicting associations between data in a first
modality and data in a
second modality using a statistical model trained to represent interactions
between data having a
plurality of modalities including the first modality and the second modality,
the statistical model
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comprising a plurality of encoders and decoders, each of which is trained to
process data for one
of the plurality of modalities, and a joint-modality representation coupling
the plurality of
encoders and decoders. The method comprises mapping the data in the first
modality and the
data in the second modality into a common representation space within the
statistical model,
accessing a statistical classifier trained using labeled data, wherein the
labeled data describes
associations between data in the first and second modalities, and predicting
associations between
the data in the first modality and the data in the second modality mapped into
the common
representation space using the trained statistical classifier.
[0008] According to another aspect of the technology described herein, some
embodiments
are directed to a computer system, comprising at least one computer processor
and at least one
storage device encoded with a plurality of instructions that, when executed by
the at least one
computer processor perform a method of training a statistical model to
represent inter-modality
associations for data, wherein the data includes data for a first modality and
data for a second
modality different from the first modality. The method comprises accessing
training data
including training data for the first modality and training data for the
second modality, training
the statistical model, the statistical model comprising first and second
encoders, first and second
decoders, and a joint-modality representation coupling the first and second
encoders to the first
and second decoders. The training comprises estimating values for parameters
of the first and
second encoders and the first and second decoders using a self-supervised
learning technique, at
least some of the training data, and information describing at least one link
between data pairs in
the training data, and storing information specifying the statistical model at
least in part by
storing the estimated values for parameters of the first and second encoders
and the first and
second decoders of the statistical model.
[0009] According to another aspect of the technology described herein, some
embodiments
are directed to a computer system comprising at least one computer processor
and at least one
storage device encoded with a plurality of instructions that, when executed by
the at least one
computer processor perform a method of predicting an association between input
data in a first
modality and data in a second modality using a statistical model trained to
represent interactions
between data having a plurality of modalities including the first modality and
the second
modality, the statistical model comprising a plurality of encoders and
decoders, each of which is
trained to process data for one of the plurality of modalities, and a joint-
modality representation
coupling the plurality of encoders and decoders. The method comprises
selecting, based on the
first modality and the second modality, an encoder/decoder pair or a pair of
encoders, from
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among the plurality of encoders and decoders, and processing the input data
with the joint-
modality representation and the selected encoder/decoder pair or pair of
encoders to predict the
association between the input data and the data in the second modality.
[0010] According to another aspect of the technology described herein, some
embodiments
are directed to a computer system comprising at least one computer processor
and at least one
storage device encoded with a plurality of instructions that, when executed by
the at least one
computer processor, perform a method of predicting associations between data
in a first
modality and data in a second modality using a statistical model trained to
represent links
between data having a plurality of modalities including the first modality and
the second
modality different from the first modality, the statistical model comprising a
plurality of
encoders and decoders, each of which is trained to process data for one of the
plurality of
modalities, and a joint-modality representation coupling the plurality of
encoders and decoders.
The method comprises mapping the data in the first modality and the data in
the second
modality into a common representation space within the statistical model,
accessing a statistical
classifier trained using labeled data, wherein the labeled data describes
associations between
data in the first and second modalities, and predicting associations between
the data in the first
modality and the data in the second modality mapped into the common
representation space
using the trained statistical classifier.
[0011] According to another aspect of the technology described herein, some
embodiments
are directed to a method for training a statistical model to represent
associations between drug
data, gene data, and disease data. The method comprises accessing training
data including gene
training data, drug training data and disease training data, and training the
statistical model, the
statistical model comprising a plurality of encoders including a gene encoder,
a drug encoder
and a disease encoder, a plurality of decoders including a gene decoder, a
drug decoder and a
disease decoder, and a joint representation coupling the plurality of encoders
to the plurality of
decoders, wherein the joint representation describes interactions between the
training data. The
training comprises estimating values for parameters of the gene encoder and
the gene decoder
using a self-supervised learning technique, the gene training data, and
information describing
interactions between data pairs in the gene training data, estimating values
for parameters of the
gene encoder, the gene decoder, the drug encoder, and the drug decoder using a
self-supervised
learning technique, the gene training data and the drug training data, and
information describing
interactions between data elements in the gene training data and data elements
in the drug
training data, estimating values for parameters of the gene encoder, the gene
decoder, the disease
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encoder, and the disease decoder using a self-supervised learning technique,
the gene training
data and the disease training data, and information describing interactions
between data elements
in the gene training data and data elements in the disease training data, and
storing information
specifying the statistical model at least in part by storing the estimated
values for parameters of
the gene encoder, the gene decoder, the drug encoder, the drug decoder, the
disease encoder, and
the disease decoder of the statistical model.
[0012] According to another aspect of the technology described herein, some
embodiments
are directed to a computer system, comprising at least one computer processor
and at least one
storage device encoded with a plurality of instructions that, when executed by
the at least one
computer processor perform a method of training a statistical model to
represent associations
between drug data, gene data, and disease data. The method comprises accessing
training data
including gene training data, drug training data and disease training data,
and training the
statistical model, the statistical model comprising a plurality of encoders
including a gene
encoder, a drug encoder and a disease encoder, a plurality of decoders
including a gene decoder,
a drug decoder, and a disease decoder, and a joint representation coupling the
plurality of
encoders to the plurality of decoders, wherein the joint representation
describes interactions
between the training data. The training comprises estimating values for
parameters of the gene
encoder and the gene decoder using a self-supervised learning technique, the
gene training data,
and information describing interactions between data pairs in the gene
training data, estimating
values for parameters of the gene encoder, the gene decoder, the drug encoder,
and the drug
decoder using a self-supervised learning technique, the gene training data and
the drug training
data, and information describing interactions between data elements in the
gene training data and
data elements in the drug training data, and estimating values for parameters
of the gene
encoder, the gene decoder, the disease encoder, and the disease decoder using
a self-supervised
learning technique, the gene training data and the disease training data, and
information
describing interactions between data elements in the gene training data and
data elements in the
disease training data, and storing information specifying the statistical
model at least in part by
storing the estimated values for parameters of the gene encoder, the gene
decoder, the drug
encoder, the drug decoder, the disease encoder, and the disease decoder of the
statistical model.
[0013] According to another aspect of the technology described herein, some
embodiments
are directed to a method for predicting a new disease indication for a given
drug. The method
comprises projecting a representation of the given drug and representations of
a plurality of
diseases into a common representation space of a trained statistical model and
predicting the
new disease indication for the given drug based on a comparison of the
projected representation
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of the given drug and at least one of the representations of the plurality of
diseases in the
common representation space.
[0014] According to another aspect of the technology described herein, some
embodiments
are directed to a computer system, comprising at least one computer processor;
and at least one
storage device encoded with a plurality of instructions that, when executed by
the at least one
computer processor, performs a method of predicting a new disease indication
for a given drug.
The method comprises projecting a representation of the given drug and
representations of a
plurality of diseases into a common representation space of a trained
statistical model, and
predicting the new disease indication for the given drug based on a comparison
of the projected
representation of the given drug and at least one of the representations of
the plurality of
diseases in the common representation space.
[0015] According to another aspect of the technology described herein, some
embodiments
are directed to a method of identifying disease indications for a given drug.
The method
comprises providing as input to a statistical model, representations of a
plurality of drugs and a
plurality of diseases, and processing the representations of the plurality of
drugs and the plurality
of diseases using a trained supervised classifier to identify a likelihood
that drugs in the plurality
of drugs will be effective in treating diseases in the plurality of diseases,
the supervised classifier
trained with information on Federal Drug Administration (FDA) approved drug-
disease pairs.
[0016] According to another aspect of the technology described herein, some
embodiments
are directed to a computer system, comprising at least one computer processor
and at least one
storage device encoded with a plurality of instructions that, when executed by
the at least one
computer processor, performs a method of identifying disease indications for a
given drug. The
method comprises providing as input to a statistical model, representations of
a plurality of
drugs and a plurality of diseases, and processing the representations of the
plurality of drugs and
the plurality of diseases using a trained supervised classifier to identify a
likelihood that drugs in
the plurality of drugs will be effective in treating diseases in the plurality
of diseases, the
supervised classifier trained with information on Federal Drug Administration
(FDA) approved
drug-disease pairs.
[0017] It should be appreciated that all combinations of the foregoing
concepts and
additional concepts discussed in greater detail below (provided such concepts
are not mutually
inconsistent) are contemplated as being part of the inventive subject matter
disclosed herein.
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Brief Description of the Drawings
[0018] Various non-limiting embodiments of the technology will be described
with
reference to the following figures. It should be appreciated that the figures
are not necessarily
drawn to scale.
[0019] FIG. 1 is a diagram of a heterogeneous network in accordance with
some
embodiments;
[0020] FIG. 2 is a diagram of a heterogeneous network of biological data
that may be
represented using a multi-modal statistical model in accordance with some
embodiments;
[0021] FIG. 3 is a diagram of a model architecture for representing a
heterogeneous network
of biological data in accordance with some embodiments;
[0022] FIG. 4 is a flowchart of a process for training a statistical model
to represent a
heterogeneous network of biological data in accordance with some embodiments;
[0023] FIG. 5 is a diagram of a process for performing data embedding in
accordance with
some embodiments;
[0024] FIG. 6 is a diagram of a process for projecting single-modality
information and
network links into a common latent space in accordance with some embodiments;
[0025] FIG. 7 shows example neural network architectures for encoders and
decoders used
in accordance with some embodiments;
[0026] FIG. 8 is a flowchart of a process for training a statistical model
to represent intra-
and inter-modality network links in a heterogeneous network in accordance with
some
embodiments;
[0027] FIG. 9 is a diagram of a process for training a statistical model to
represent intra-
modality network links in accordance with some embodiments;
[0028] FIGS. 10A-10C are diagrams of processes for training a statistical
model to represent
inter-modality network links in accordance with some embodiments;
[0029] FIG. 11 schematically illustrates making a multi-modal prediction
using a trained
multi-modal statistical model in accordance with some embodiments;
[0030] FIG. 12 shows a process for making unsupervised predictions in a
modality-specific
representation space in accordance with some embodiments;
[0031] FIG. 13 schematically illustrates a technique for comparing
positions of embedding
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and projected vectors in a modality-specific representation space in
accordance with some
embodiments;
[0032] FIG. 14 shows a process for making unsupervised predictions in a
joint-modality
representation space in accordance with some embodiments;
[0033] FIG. 15 shows a process for making supervised predictions using a
trained multi-
modal statistical model in accordance with some embodiments; and
[0034] FIG. 16 shows components of an illustrative computer system on which
some
embodiments may be implemented.
Detailed Description
[0035] Conventional computational approaches to predict associations
between biological
data (e.g., drug-disease matches) using statistical or machine learning
techniques typically
employ supervised learning techniques. The data set available for training
such techniques is
often limited to a relatively small amount of labeled data (e.g., FDA approved
drugs). Such
approaches are also typically focused on one or two modalities (e.g., drugs
and diseases), and do
not consider information from other modalities during training or in making
predictions. To this
end, some embodiments are directed to a scalable technique for integrating
biological
information from multiple modalities to incorporate biological (e.g., drug
and/or disease)
information from a wide range of sources. In particular, some embodiments are
directed to
representing a heterogeneous network of multimodal biological information
using one or more
statistical models configured learn connections between the data in the model
using a self-
supervised learning technique. A schematic example of a heterogeneous network
that may be
represented using a statistical model in accordance with some embodiments is
shown in FIG. 1.
[0036] As shown, heterogeneous network 100 includes a plurality of nodes
and connections
between the nodes. Each of the nodes in the network 100 is associated with
data having a
different modality. For example, node A may represent data associated with
diseases, node B
may represent data associated with genes, and node C may represent data
associated with drugs.
The links associated with the nodes in network 100 include intra-modality
links (e.g., links 132,
134) that describe interactions between data within a single modality. For
example, link 132
describes an interaction between data associated with node B (e.g., genes
interacting with other
genes) and link 134 describes an interaction between data associated with node
C (e.g., drugs
having structural similarity to other drugs). Each node in the heterogeneous
network may
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include any suitable number of intra-modality links (including no intra-
modality links), and the
number of links associated with any one node in the network may be dependent
on the modality
of the data associated with the node. For example, as discussed in more detail
below, a node
associated with the "gene" modality may have more intra-modality links than a
node associated
with the "drug class" modality.
[0037] Each node in network 100 also includes at least one inter-modality
link (e.g., links
112, 114, 116 and 122) that describe an interaction between data from
different modalities. The
inter-modality link(s) connect the node to other node(s) in the network.
Whereas some nodes
only include a single inter-modality link, other nodes include multiple inter-
modality links to
one or more other nodes indicating more complex associations between the data
in network 100.
By virtue of the inter-modality links in network 100, associations between
data from disparate
data sources in the network may be learned in some embodiments to enable
predictions between
nodes that are directly or indirectly connected via other nodes in the
network. For example, the
association between data in node A and node C may be learned via the direct
link 116 between
these two nodes as well as indirect paths between node A and node C via node B
(e.g., via links
112, 114 and 122). The mesh of learned connections between data represented by
the nodes in
network 100 adds to the richness of the data representation encoded using a
trained statistical
model in accordance with some embodiments. For example, the trained
statistical model may be
used to predict missing links within the heterogeneous drug-disease network.
[0038] FIG. 2 shows an example of a drug-disease heterogeneous network that
may be
represented using a statistical model in accordance with some embodiments. As
shown, the
network includes a plurality of nodes, each of which is associated with
biological data for a
different modality. The network includes intra-modality and inter-modality
links associated
with and connecting the nodes in the network. The links describe how pairs of
data within a
modality or from different modalities are related to each other. By including
multiple nodes in a
heterogeneous network, relationships between drugs and diseases can be
established through
multiple modalities, such as genes affected by a disease or associated with
disease, genes
regulated by drugs or targeted by drugs, and genes expressed in disease-
affected tissues.
Additionally, drugs can be characterized by their molecular structure, their
respective protein
targets, drug class, and side effects, whereas diseases can also be
characterized by disease
ontology.
[0039] In the particular drug-disease network shown in FIG. 2, the node
associated with
genes represents core functional links between drugs and diseases by being
connected directly
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with four other nodes in the network. Genes may be characterized by functional
interactions,
such as protein-protein interactions, transcriptional regulation or co-
expression networks, and
their respective biological pathways or gene ontology associations. In some
embodiments, the
network includes one or more of drug- and disease- metabolomics, proteomics,
and microbiome
information.
[0040] As additional biological data becomes available, the drug-disease
heterogeneous
network shown in FIG. 2 may be expanded to include additional nodes and/or
additional links
between the nodes. In such a way, the representation of the drug-disease
heterogeneous network
is easily extensible and scalable, unlike some conventional computational
models trained to
make predictions based on data from only one or two modalities. New nodes or
types of data
represented within existing nodes of the heterogeneous network may be added in
any suitable
way. For example, in some embodiments, nodes within the drug-disease
heterogeneous network
may include data associated with different organisms (e.g., data from human
and mouse
datasets). Drug-phenotype associations from model organisms, from
Saccharomyces cerevisiae
(yeast), Caenorhabditis elegans (worm), Danio rerio (zebrafish), Arabidopsis
thaliana (thale or
mouse-ear cress) and Drosophila melanogaster (fruit fly) may also be included.
In another
example, inter-organism connections may be represented in the model using
orthologous gene
associations.
[0041] The data associated with the nodes in the heterogeneous network may
be identified
from any data source that provides reliable information about the interactions
between data
within a particular modality (e.g., gene-gene interactions) or between data
from different
modalities (e.g., drug treatments for diseases). In some embodiments,
information about the
interactions of data with the heterogeneous network are determined based on
information in
publically-accessible databases and/or proprietary databases of biological
information or based
on the results of clinical trials or other medical research. For example, data
associated with
drugs may include information related to small molecules and/or biologics and
data associated
with diseases may include information related to disease categories including,
but not limited to,
neoplasms (e.g., leukemia, lymphoma, lung cancer, melanoma, thyroid cancer,
hepatic cancer,
prostate cancer, kidney or renal cancer, pancreatic cancer, intestine cancer,
glioblastoma,
astrocytomas, breast cancer, among others) and non-cancer diseases (e.g.,
neurological,
cardiovascular, dermatological, musculoskeletal, urologics, respiratory,
nutritional and
metabolic diseases, etc.).
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[0042] A drug-disease heterogeneous network used in accordance with some
embodiments
may also include information related to gene-gene interactions derived from
synthetic lethal
screens and gene-disease interactions derived from Crispr- or shRNA or siRNA
screening.
Additionally, information about direct interactions between drugs and diseases
may be
determined based, at least in part, on information about FDA approved drugs -
disease
indications and in vitro cancer cell line viability experiments.
[0043] Table 1 provides a listing of example datasets and databases that
may be used to
identify data and interactions for a heterogeneous network in accordance with
some
embodiments. As described in more detail below, information about interactions
between data
extracted from these data sources (and others) may be used to train a
statistical model such that
the trained statistical model is configured to represent inter-modality
associations in the
heterogeneous network. The trained statistical model may then be used to make
new inter-
modality predictions.
Dataset Database
Drug expression profiles CMAP-LINCS-L1000
Drug targets, structure, and class ChEMBL, ChemSpider, PubChem, DrugsDB,
DrugCentral
Disease expression profile TOGA
Disease-gene association (mutation) COSMIC db, OMIM db, intogen db
Disease-anatomy association Medline V1.0 (Himmelstein DS. 2016)
Gene-Pathway associations KEGG, Reactome, WikiPathway, Gene Ontology
Gene-Anatomy association/regulation GTEx Portal, TISSUES, Bgee
Protein-Protein interactions StringDB, Human Interaction Database, and
the
Human Protein Reference Database
Gene regulatory interactions CMAP-LINOS-L1000
Table 1: Example databases used for building a drug-disease heterogeneous
network.
[0044] As discussed above in connection with FIG. 2, each node in the
heterogeneous
network includes at least one link to one or more other nodes in the network.
Some
embodiments are directed to encoding these links between data in the network
by training a
statistical model using information about pairs of data extracted from data
sources including, but
not limited to, the data sources listed in Table 1.
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[0045] Each of the nodes and its associated links (both intra-modality and
inter-modality) in
the network of FIG. 2 may be considered separately for training a statistical
model in accordance
with some embodiments. Each of the links between data for nodes in the network
may be
represented using categorical features. The categorical features enable the
data for each
modality to be mapped to a vector having continuous values using a data
embedding technique
described in more detail below. The vectors are then provided as input to the
statistical model
during a training phase and may be used for prediction following training.
[0046] In some instances, interactions between data in the heterogeneous
network may be
represented using only categorical features. For example, in the interaction
"drug-treats-
disease," a particular drug may either be approved to treat a particular
disease or not approved.
In other words, the "treats" interaction is binary. In other instances,
interactions between data in
the heterogeneous network may additionally be represented using numerical
features that
indicate a strength of the interaction between the linked data. For example,
in the interaction
"drug-regulates-gene," categorical features may be used to represent whether a
particular drug
regulates a particular gene based, for example, on drug expression profiles,
and numerical
features may be used to represent the extent or strength of the regulation as
determined, for
example, based on differential gene expression comparisons.
[0047] Example interactions associated with the heterogeneous network shown
in FIG. 2 are
described in more detail below including an indication of which data from the
example
databases in Table 1 was used to determine the interaction data and whether
the interaction was
represented in the heterogeneous network using only categorical features or
numerical features
in addition to categorical features. The interactions in the network of FIG. 2
are described below
by computing interaction metrics in exemplary ways. However, it should be
appreciated that
any or all of the interaction metrics may be extracted and/or computed from
data sources in any
suitable way, and embodiments are not limited in this respect.
Drug-centered interactions
[0048] As shown in FIG. 2, the "drug" node includes six different drug-
centered interactions
including one intra-modality interaction (drug-resembles-drug) and five inter-
modality
interactions that connect the drug node to other nodes in the network. The
intra-modality "drug-
resemble-drug" interaction, which is defined by both categorical and numerical
features,
describes pairwise structural similarities of drugs in the network. For
example, the "resemble"
metric may be computed by calculating the pairwise drug structure similarity
from drug-
corresponding fingerprints, based on the Tanimoto coefficient and using the
python library
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RDKit (http://www.rdkit.org). In one implementation, drug structures were
downloaded from
three different databases (ChEMBL, ChemSpider, PubChem) in the form of
"smiles", followed
by smile standardization using the python library MolVS
(https://molvs.readthedocs.io/). Next,
the molecular fingerprints for each drug were computed and the Tanimoto
coefficient from all
possible pairwise drug fingerprints comparisons was calculated to determine
which drugs
resembled other drugs.
[0049] The "drug-regulates-gene" interaction is defined by both categorical
and numerical
features. This interaction may be determined based on drug expression profiles
extracted, for
example, from the CMAP-LINCS-L1000 database. In one implementation, the data
was
downloaded from the Gene Expression Omnibus database (Accession ID =
GSE92742), and
contained a total of 19811 drugs that were screened in triplicate at two
different time points (6
hours and 24 hours) in a variable set of 3-77 well annotated cell lines. The
gene expression data
used in this implementation included level 5 processed data, containing for
each cell line, time
point and drug treatment, the normalized differential gene expression values
with respect to the
control conditions. The data may be represented by a vector (e.g., of
dimension lx12328) of
genes and their corresponding Z-scores for each combination of cell line, time
point and drug
treatment.
[0050] Additionally, drug-induced gene expression data was generated for
multiple drugs
from a proprietary database. These profiles were generated in seven different
cancer cell lines,
at two different time points (6 hours and 24 hours) and at two different
concentrations for each
drug. The differential gene expression was normalized with respect to the
control condition, and
processed in the form of a Z-score. The data generated for drugs from the
proprietary database
had the same structure as the CMAP-LINCS-L1000's data.
[0051] As noted above, the "drug-treats-disease" interaction is
categorical. This interaction
may be based on a list of approved (e.g., FDA approved) drugs and their
corresponding disease
indications. In one implementation, data for this interaction was downloaded
from the
PharmacotherapyDB database and contained 755 disease-drug pairs.
[0052] The "drug-includes-drug class" interaction is categorical. This
interaction describes
the correspondence between each drug and its pharmacologic class. In one
implementation, data
for this interaction was downloaded from the DrugBank
(https://www.drugbank.ca/) and
DrugCentral (http://drugcentral.org) databases.
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[0053] The "drug-binds-gene" interaction is categorical. This interaction
describes the
relationship between drugs and their protein targets, encoded by genes. In one
implementation,
data for this interaction were obtained from the DrugBank
(https://www.drugbank.ca/),
DrugCentral (http://drugcentral.org), and BindingDB
(https://www.bindingdb.org) databases.
Disease-centered interactions
[0054] As shown in FIG. 2, the "disease" node includes five different
disease-centered inter-
modality interactions (one of which is the "drug-treats-disease" interaction
described above) that
connect the disease node to other nodes in the network. The disease node is
not associated with
any intra-modality interactions. The "disease-regulates-gene" interaction is
represented using
both categorical and numerical features. In one implementation, data for this
interaction was
obtained from the TCGA database (https://tcga-data.nci.nih.gov/) and from a
proprietary
database. This interaction relates to genes that are up- and down-regulated in
diseased tissue
when compared to matching normal control tissue or healthy individuals. The
TCGA database
contains cancer gene expression profiles and their matching normal control
tissue profile for
each patient. In one implementation, both profiles for each patient were
downloaded, the
corresponding fold change between tumor and control was calculated, and the
gene expression
values were normalized to Z scores. A proprietary database containing
approximately 1500
gene expression profiles from 575 different diseases (cancer and non-cancer
disease indications)
was also used to generate data for the "disease-regulates-gene" interaction.
Data from the Gene
Expression Omnibus Database (https://www.ncbi.nlm.nih.gov/geo/) was downloaded
and
processed using the R libraries GEOquery and Limma. Each disease expression
profile was
normalized with Limma, followed by gene fold change calculation between
disease and normal
cases. Proprietary disease gene expression profiles were also normalized to Z-
scores.
[0055] The "disease-associates-gene" interaction is categorical. This
interaction relates to
gene-specific mutations associated to a particular disease. In one
implementation, the
associations of gene mutations corresponding to Mendelian diseases were
downloaded from the
OMIM database (https://www.omim.org/). The associations of gene mutations
corresponding to
specific cancers were downloaded from the COSMICdb
(https://cancer.sanger.ac.uk/cosmic) and
Intogen databases (https://www.intogen.org/).
[0056] The "disease-localizes-anatomy" interaction is categorical. This
interaction relates to
the association between diseases and corresponding human tissues affected by
disease. In one
implementation, these relationships were downloaded from the Medline disease-
tissue
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association (Himmelstein DS. 2016) database. Anatomical terms were mapped to
anatomical
structures ontology terms (http://uberon.github.io, Mungall et al, 2012).
Gene-centered interactions
[0057] As shown in FIG. 2, the "gene" node includes thirteen different gene-
centered
interactions including three intra-modality interactions and ten inter-
modality interactions (six of
which are described above in connection with the drug- and disease-centered
interactions) that
connect the gene node to other nodes in the network. The intra-modality "gene-
interacts with-
gene" interaction is categorical and relates to physical protein-protein
interactions downloaded,
for example, from StringDB (https://string-db.org/), the Human Interaction
Database
(http://interactome.dfci.harvard.edu/), and the Human Protein Reference
Database
(http://www.hprd.org).
[0058] The intra-modality "gene-regulates-gene" interaction is represented
using both
categorical and numerical features. This interaction relates to normalized
gene expression levels
across different cancer cell lines with respect to knockdown or overexpression
of specific genes.
In one implementation, this data was downloaded from CMAP-LINCS-L1000, and the
gene
expression values were normalized in Z-scores.
[0059] The intra-modality "gene-covaries with-gene" interaction is
represented using both
categorical and numerical features. This interaction relates to the rate of
evolutionary
covariation between genes. In one implementation, the data for this
interaction was downloaded
from Priedigkeit et al, 2015. Insight for including this interaction in the
network is derived from
the observation that genes that tend to co-evolve together are generally
involved in similar
biological pathways and therefore may participate in similar diseases.
[0060] The "gene-expresses in-anatomy" interaction is categorical and
includes expression
levels of genes in specific human tissue types. In one implementation, data
for this interaction
were downloaded from the TISSUES database (https://tissues.jensenlab.org/) and
the GTEx
Portal (https://www.gtexportal.org/). The TISSUES database combines data from
gene
expression, immunohistochemistry, proteomics and text mining experiments,
whereas the GTEx
Portal contains RNA-sequence data from multiple human tissues.
[0061] The "gene regulated by anatomy" interaction is categorical and
includes gene
regulation information (e.g., up- and down-regulation) in specific tissue
types. In one
implementation, data for this interaction were extracted from the Bgee
database, for adult
humans (https://bgee.org/) and the GTEx Portal.
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[0062] The "gene-participates in-pathway" interaction is categorical and
relates to the
association between genes and their corresponding cellular pathways. In one
implementation,
the molecular function, cellular localization and biological process were
downloaded from the
Gene Ontology Consortium (http://www.geneontology.org). The associations
corresponding to
metabolic, and signaling pathways were obtained from KEGG
(www.genome.jp/kegg/),
Reactome (https://reactome.org), and WikiPathways (https://wikipathways.org/).
[0063] Although six nodes are shown in the illustrative heterogeneous
network of FIG. 2, it
should be appreciated that a heterogeneous network including additional (or
fewer) nodes may
alternatively be represented using one or more statistical models in
accordance with some
embodiments. For example, some embodiments are directed to representing a
heterogeneous
network including only the three nodes "drug," "gene," and "disease" and their
corresponding
intra- and inter-modality links by a statistical model. In other embodiments,
a heterogeneous
network having at least one node representing patient data (e.g., from an
electronic health
record) is represented using a statistical model.
[0064] Some embodiments are directed to a multi-modal representation that
integrates all
domains and modalities from a heterogeneous network of biological data, an
example of which
is described above in connection with FIG. 2. Unlike some conventional
approaches that rely on
supervised learning and a limited training data set, some embodiments employ
self-supervised
learning techniques that do not require large paired datasets for training. As
discussed in more
detail below, the statistical model is trained in some embodiments to take
advantage of shared
connections between drugs and diseases, such as genes, in order to find novel
drug-disease
associations.
[0065] FIG. 3 schematically illustrates a high-level architecture of a
statistical model that
may be trained using self-supervised learning techniques in accordance with
some embodiments.
Each of the nodes corresponding to a different modality in a heterogeneous
network is
represented as a separate path from input to output through the architecture.
Only "gene,"
"drug," and "disease" modalities are represented in the architecture of FIG.
3. However, it
should be appreciated that other modalities including, but not limited to, the
other nodes in the
heterogeneous network of FIG. 2, may also be included in the model
architecture shown in FIG.
3.
[0066] As shown, the architecture of FIG. 3 includes a plurality of
encoder/decoder pairs,
each of which is configured to employ a self-supervised learning technique to
train values for
parameters of the unimodal encoder/decoder pair. The number of encoder/decoder
pairs
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included in the architecture depends on the number of modalities or nodes
included in the
heterogeneous network. The encoder/decoder pairs are joined using a common
latent space
(also referred to herein as a joint-modality representation or joint
multimodal representation) to
form a multi-modal statistical model that is able to learn joint
representations of each network
node and its corresponding network links, as described in more detail below.
[0067] As shown in FIG. 3, for each encoder/decoder pair, the architecture
includes a
plurality of embedding representations, which are vectors of continuous values
that are a
transformation of the categorical input data. The encoders and decoders in
each
encoder/decoder pair are coupled via a joint-modality representation, which
includes joint
representation vectors of connected network nodes in the heterogeneous
network. The number
of vectors in the joint-modality representation is equal to the number of
interactions in the
network such that the joint-modality representation may be represented as an
NxD matrix, where
N is the number of interactions in the network and D is a length of each joint
representation
vector. In some embodiments, N> 1x106. Information about interactions between
data in the
network is encoded in the joint-modality representation. The interactions may
be encoded in
any suitable way. In some embodiments, an embedding interaction vector
representing a
particular interaction between data in an input pair may be created and
concatenated to a
corresponding joint representation vector in the common latent space. In other
embodiments,
rather than concatenating an embedding interaction vector to the joint
representation vector, the
embedding interaction vector may be concatenated to the output from two
encoders from which
the joint representation vector is created. In yet other embodiments, the
interaction information
may be intrinsically encoded by virtue of a joint representation vector being
formed from the
output of two encoders to which particular input data having a particular
interaction was
provided.
[0068] As discussed in more detail below, for intra-modality (e.g., gene-
gene) interactions,
each of the encoder/decoder pairs is trained using a self-supervised learning
technique, pairs of
input data within the modality associated with a node in the heterogeneous
network, and
interaction information describing an interaction between the pairs of data.
For inter-modality
(e.g., gene-drug) interactions, two encoder/decoder pairs are trained using a
self-supervised
learning technique, pairs of input data across the two modalities, and
interaction information
describing an interaction between the input data from the different
modalities. When the
interaction includes both categorical and numerical features, the numerical
features may be
taken into account by, for example, multiplying the embedding interaction
vector and/or all or a
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portion of the joint representation vector by a value corresponding to the
strength or degree of
the interaction as represented in the numerical features.
[0069] FIG. 4 illustrates a process 400 for training a multi-modal
statistical model having an
architecture shown in FIG. 3, in accordance with some embodiments. In act 410,
training data
(e.g., extracted from one or more public or proprietary data sources such as
those in Table 1) is
converted into embedding vectors that are to be provided as input to encoders.
During data
embedding, related categorical variables are represented by dense vectors of
real numbers that
capture the relationship between them. The embedding vectors represent each
variable in a
continuous numerical space. Creation of embedding vectors are described in
more detail in
connection with FIG. 5.
[0070] Process 400 then proceeds to act 412, where the embedding vectors
are provided as
input to a modality-specific encoder to provide an encoded output vector in
the joint-modality
representation space. Process 400 then proceeds to act 414, where a joint
representation vector
is computed based, at least in part, on the encoded output vectors output from
two encoders.
The joint representation vector may additionally be computed based, at least
part, on information
describing an interaction between the input data, such as an embedding
interaction vector, as
described above. Process 440 then proceeds to act 416, where the joint
representation vector is
provided as input to a modality-specific decoder to generate a decoded output
vector. Process
400 then proceeds to act 418, where the weights in the encoders and decoders
are updated based,
at least in part, on a comparison of the decoded output vector and the
embedded vector provided
as input to the modality-specific encoder. For example, a self-supervised
learning technique is
used to update values of parameters (e.g., weights) in the encoder and decoder
during training.
Each of the acts described in process 400 is described in more detail below.
[0071] FIG. 5 shows a process for generating embedding vectors for input
data associated
with a node in a heterogeneous network using categorical features in
accordance with some
embodiments. An input dimension V is defined for each modality that
corresponds to the size of
the vocabulary of the data in the modality. In the example shown in FIG. 5,
the modality is
"gene" and the size of the vocabulary V is 20,000 indicating that there are
20,000 genes in the
input dataset. Each element of the modality is "represented" by a one-hot
vector 510 of length
V, with the ith element having a value of 1, with all other elements in the
vector being set to 0.
For example, to encode the input data element "Gene A," the value of position
153 in the one-
hot vector 510 is set to 1, while all of the other values in the vector are
set to 0. A separate one-
hot vector is created for each of the elements (e.g., each of the 20,000 genes
in the example of
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FIG. 5) in the input data set for the modality. The one-hot vectors 510 are
then projected into a
lower dimensional embedding space of size 1xE that contains a continuous
numerical
representation of the input variable, rather than a binary value. In the
example shown in FIG. 5,
E=10, though it should be appreciated that E may be set to any other suitable
value and
embodiments are not limited in this respect.
[0072] In some embodiments, data embedding is accomplished by transforming
the one-hot
vectors corresponding to each modality element with an embedding matrix 520 of
dimensions
VxE to produce a plurality of embedding vectors 530, each of which corresponds
to a different
one of the input data elements (e.g., Gene A in the example of FIG. 5). In
some embodiments,
the values of embedding matrix 520 are randomly initialized from a uniform
distribution with
range of -1/V and +1/V. During training of the statistical model the values
for parameters of
embedding matrix 520 may remain fixed or alternatively may be updated as part
of the training
process. By updating the parameter values for embedding matrix 520 during
training, it is
expected that the embedding vectors 530 for connected nodes in the
heterogeneous network will
be closer in the embedded representation space than non-connected nodes.
[0073] In some embodiments, network links between the nodes in the
heterogeneous
network are also embedded using a similar embedding procedure as described
above, but may
have a lower embedding dimension (e.g., 1x5) compared to the dimension of the
embedding
vectors 530. FIG. 6 schematically illustrates an example of how network links
may be encoded
in some embodiments. In particular, FIG. 6 illustrates how embedding vectors
530 produced as
output of the data embedding architecture described in connection with FIG. 5
are projected into
a common latent space 650 using an encoder 602. Common latent space 650 is
also referred to
herein as a joint-modality representation. As shown, encoder 602 maps each
embedding vector
530 to a higher-dimensional latent representation vector 604 within the common
latent space
650. In the example of FIG. 6, encoder 602 maps each of the embedding vectors
from a
dimensionality of 1x10 to a dimensionality of 1x95 in the common latent space
650. It should
be appreciated however, that the output dimensionality of encoder 602 may take
any suitable
value. An example architecture for encoder 602 is described in more detail
below in connection
with FIG. 7.
[0074] FIG. 6 also illustrates that information about the network links is
also projected into
the common latent space 650 in accordance with some embodiments. In an
embedding process
similar to that discussed in accordance with FIG. 5, information about network
links in a
heterogeneous network may be embedded by creating one-hot vectors 610
corresponding to each
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network link element for a particular node in the network. FIG. 6 shows an
example of
embedding network links for the "gene" node in the heterogeneous network shown
in FIG. 2.
One-hot vector 610 includes nine elements, each of which represents one of the
nine types of
intra-modality or inter-modality network links associated with the "gene" node
in FIG. 2. As
shown, a one-hot vector with the fifth element being set to 1 and all of the
other elements set to
0 may be used, for example, to embed the "interacts" link corresponding to the
"gene-interacts-
gene" network link. The dimension I of the one-hot vector 610 is based on the
number of types
of network links associated with each node in the network.
[0075] Each of the one-hot vectors may be mapped using an embedding matrix
620 of
dimensions IxF to produce a plurality of embedding interaction vectors 630,
each of which
corresponds to one of the input data elements. As described above, in some
embodiments F<E
such that the dimensionality of the embedding interaction vectors 630 is less
than the
dimensionality of the embedding vectors 530. In some embodiments, the values
of embedding
matrix 620 are randomly initialized from a uniform distribution with range of -
1/I and +1/I.
During training of the statistical model the values for parameters of
embedding matrix 620 may
remain fixed or alternatively may be updated as part of the training process.
In the example
architecture of FIG. 6, the information about network links is represented in
the common latent
space 650 by concatenating a latent representation vector 604 and an embedding
interaction
vector 634 output from the network link embedding process, where the
concatenated vector in
the common latent space 650 represents both modality-specific data and network
link
information for the modality-specific data.
[0076] As described above, some embodiments employ a self-supervised
learning technique
using pairs of encoders/decoders for each modality or node included in the
network. In the self-
supervised learning technique, a deep neural network is trained to learn or
reproduce an input X
based on the reconstruction error between X and the output X'. Training the
parameters of the
encoders enables the encoders to reconstruct higher-level representations of
input vectors,
whereas training the decoders enables the decoders to recover the input
vectors from higher-
level representations.
[0077] As described in connection with the architecture of FIG. 6, the
inputs of the encoders
are the embedding vectors 530 of network nodes, for each variable or element
of each modality.
The encoders map each embedding vector into a higher dimensional latent
representation 604.
In some embodiments, the encoders can be characterized by
Z=a(WeX+be) (Equation 1)
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where X is the embedding input vector 530, Z is the output vector or latent
representation 604, We and be represent linear weights and bias, respectively,
and a is an
activation function. In some embodiments, the activation function is a non-
linear activation
function, for example, a Rectified Linear Unit (ReLU), Exponential Linear Unit
(ELU) or leaky
ReLu activation function.
[0078] FIG. 7 illustrates an example architecture for an encoder 620 that
may be used in
accordance with some embodiments. In the example shown in FIG. 7, encoder 620
is
implemented as a fully connected neural network with one hidden layer, and
dimensions 10
(input layer) -> 50 (hidden layer) -> 95 (output layer). The output layer of
the encoder 620 is a
joint representation vector in the common latent space 650.
[0079] The decoder portion of each encoder/decoder pair is configured to
map the latent or
joint representation of two interacting nodes (Z) in the heterogeneous network
back to the
embedding representation vector of input variables or individual network nodes
(X'). In some
embodiments, decoders can be characterized by
,
X = a(WdZ + bd) (Equation 2)
where Wd and bd represent linear weights and bias, respectively, and a is an
activation
function. In some embodiments, the activation function is a non-linear
activation function, for
example, a Rectified Linear Unit (ReLU), Exponential Linear Unit (ELU) or
leaky ReLu
activation function.
[0080] FIG. 7 also illustrates an example architecture for a decoder 720
that may be used in
accordance with some embodiments. In the example shown in FIG. 7, decoder 620
is
implemented as a fully connected neural network with one hidden layer, and
dimensions 100
(input layer) -> 50 (hidden layer) -> 10 (output layer). The output layer of
the decoder 720 is a
decoded vector X' having the same dimensionality as the embedding vector X
provided as input
to the encoder 620.
[0081] Having discussed a general architecture for components of a multi-
modal statistical
model that may be used to represent a heterogeneous network of biological
data, examples of
training the multi-modal statistical model to learn the associations between
data in nodes of the
network are provided below.
[0082] FIG. 8 shows a flowchart of a process 800 for training a multi-modal
statistical
model in accordance with some embodiments. The particular training techniques
used may
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depend on the types of interactions between the data in the nodes of the
heterogeneous network
that are to be represented in the model. In act 810, the modality-specific
embedding vectors are
created using the data embedding processes described above. In embodiments
that also create
embedding interaction vectors for concatenation in the common latent space,
such embedding
interaction vectors may also be created in act 810 using the embedding
techniques described
herein.
[0083]
Process 800 then proceeds to act 812, where the multi-modal statistical model
is
trained to learn intra-modality interactions for each of the nodes in the
heterogeneous network
that includes at least one intra-modality interaction. For example, in the
heterogeneous network
shown in FIG. 2, only the "gene" and "drug" nodes are associated with intra-
modality links.
Accordingly, for each of these nodes, the multi-modal statistical model may be
separately
trained to learn the corresponding intra-modality network links for the node.
An example of
training the multi-modal statistical model to learn intra-modality network
links is described in
more detail below in connection with FIG. 9. It should be appreciated that
some heterogeneous
networks may not include any nodes associated with intra-modality links and
that, for such
network, training intra-modality links in act 812 may be omitted.
[0084]
Process 800 then proceeds to act 814, where the multi-modal statistical model
is
trained to learn inter-modality interactions describing relationships between
data in different
connected nodes in the heterogeneous network. As described above, each of the
nodes in the
heterogeneous network is connected to at least one other node in the network
via one or more
inter-modality network links. For each of these network links, training in act
814 is repeated
until the multi-modal statistical model has been trained on all of the network
links in the
heterogeneous network. An example of training the multi-modal statistical
model to learn inter-
modality links is described in more detail below in connection with FIGS. 10A-
C. Although act
814 is illustrated following act 812, it should be appreciated that training
of intra-modality links
and inter-modality links may be performed for the nodes of the heterogeneous
network in any
suitable order including, but not limited to, training on all intra-modality
links before training on
inter-modality links, training on all inter-modality links before training on
intra-modality links,
and interspersing the training of intra-modality and inter-modality links.
[0085]
Process 800 then proceeds to act 816, where parameters for the trained
statistical
model estimated during training are stored for use in performing prediction
tasks. Although act
816 is shown following acts 812 and 814, it should be appreciated that
estimated parameters for
the trained statistical model may be stored after one or more training
iterations in acts 812 or 814
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such that the estimated parameters determined in one training iteration are
used to initialize at
least some of the parameters of the model for a subsequent training iteration.
As an example, a
first training iteration may be focused on training the "gene-interacts-gene"
network link with
the result of the training being a gene encoder and a gene decoder with
estimated parameters that
reflect this intra-modality interaction. The estimated parameters for the gene
encoder and gene
decoder may be stored and used to initialize model parameters for a subsequent
training iteration
focused on training the "drug-binds-gene" network link. During the subsequent
training
interaction the estimated parameters for the gene encoder/decoder are further
refined from the
previously-stored values to reflect associations associated with inter-
modality training.
Examples of propagation of estimated model parameters from one training
iteration to a
subsequent training iteration are discussed in more detail below.
[0086] FIG. 9 schematically illustrates a process for training a multi-
modal statistical model
to learn the network link "gene-interacts-gene" in accordance with some
embodiments. As
shown in FIG. 9, two gene encoder/decoder pairs are shown as being
simultaneously trained.
Although shown as two separate networks for purpose of illustration, it should
be noted that
each of the gene encoder pair and the gene decoder pair illustrated in FIG. 9
correspond to a
single network structure, examples of which are shown in FIG. 7. The single
network structure
for the gene encoder and the gene decoder include parameters (e.g., network
weights) that are
estimated and updated during training using the self-supervised learning
techniques described
herein.
[0087] As shown, coupling the outputs of the encoders and inputs of the
decoders is a joint
representation, which represents the intra-modality network links on which the
multi-modal
statistical model is trained. FIG. 9 shows training of a network link that
encodes an interaction
between a first gene RPTOR and a second gene MTOR based on data sourced, for
example,
from at least one of the data sources listed in Table 1. Each of the genes
RPTOR and MTOR is
represented in the model as embedding vectors (e.g., having dimension lx10)
using the data
embedding techniques described above. Optionally, the network link
("interacts" in the example
of FIG. 9) to be trained for the gene-gene pair is also represented as an
embedded interaction
vector (e.g., having dimension 1x5) as described above.
[0088] The embedding vectors for RPTOR and MTOR are provided as input to
the instances
of the gene encoder, which encode the embedding vector representation for each
gene into a
corresponding intra-modality representation vector (e.g., having dimension
1x95) in the
common latent space. In embodiments in which the network link is also
represented as an
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embedding interaction vector, the intra-modality representation vectors for
the "connected"
input data (i.e., the data for genes RPTOR and MTOR in FIG. 9) may be
concatenated with the
embedding interaction vector in the common latent space as shown, resulting in
two
concatenated vectors (e.g., having dimensions lx100).
[0089] A joint representation vector representing the connected input data
and the network
link characterizing the connection is computed based on the two intra-modality
representation
vectors (optionally concatenated with the network link information) in the
common latent space.
For example, in some embodiments, the joint representation vector is computed
by calculating
the average or product of the two intra-modality representation vectors in the
common latent
space. In this implementation the joint representation vector has the same
dimension as the
concatenated vectors (i.e., lx100 in the example of FIG. 9). As an alternative
to the procedure
shown in FIG.9 for computing a joint representation vector, the joint
representation vector may
be computed in some embodiments based on a combination of the two intra-
modality
representation vectors (e.g., using averaging or a product) prior to
concatenation with the
embedding interaction vector describing the network link, and the embedding
interaction vector
may be concatenated with the joint representation vector following its
creation. In such a
scenario the joint representation vector may initially have a dimension the
same as the individual
intra-modality representation vectors (e.g., 1x95), with the final dimension
of the joint
representation vector being larger (e.g., lx100) following concatenation.
[0090] The training process in FIG. 9 proceeds by providing the joint
representation vector
(e.g., having dimension lx100) as input to the gene decoder (represented in
FIG. 9 as two gene
decoders for illustration), which is configured to output decoded vectors
(e.g., having dimension
lx10) for each of the input genes RPTOR and MTOR. A deviation between the
decoded vectors
output from the decoders and the embedding input vectors provided as input to
the encoders is
measured and used to update the weights in the statistical model such that the
model learns the
associations between the data in a self-supervised way. In some embodiments,
the self-
supervised learning technique is implemented using a negative sampling loss
function, and the
error determined from the negative sampling loss function is backpropagated
through the
encoders and decoders (and optionally the embedding matrices used for data
embedding) to
update the estimates of the parameters (e.g., weights) for each of these
components of the model.
[0091] The negative sampling loss function enforces the encoder/decoder
pairs to segregate
real from random network connections in accordance with the relation below.
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eTh
1)
x
..
tog eqc - vi) V, F , , ,, itoe. at ---wi . wil
.,,
. = , 4.....oe . ..*Wrs'IN.: 11 ;k
`.63;= ,, s
W i
. 1
..,
CI)
MA ) $ml
where w and c represent the connected network nodes, and wi represents an
unrelated
network node.
[0092] When the network link being encoded is an intra-modality network
link, as is the
case in the example of FIG. 9, errors determined based on both input/output
pairs are considered
when determining how to update the estimates of the parameters for the single
modality encoder
representation. Stated differently, the parameters of both of the gene
encoder/decoder
instantiations illustrated in FIG. 9 would be updated in the same way for each
backpropagation
cycle.
[0093] As discussed briefly above, some embodiments first train the
statistical model to
learn the intra-modality network links followed by training on the inter-
modality network links.
In the case of network nodes already encoded in a previous training iteration,
the parameters
stored for the pre-trained representations of the network components (e.g.,
encoders, decoders,
embedding matrices) may be used in subsequent training iterations using
different inputs.
[0094] FIG. 10A shows an example for training a multi-modal statistical
model to learn an
inter-modality interaction for a heterogeneous network in accordance with some
embodiments.
In particular, FIG. 10A shows how the statistical model may be trained to
learn the "drug-binds-
gene" network link in the heterogeneous network shown in FIG. 2. The training
process
outlined in FIG. 10A is similar to that described in FIG. 9 for training an
intra-modality network
interaction, with the primary differences being the inputs and the
encoders/decoders used for the
training. Briefly, embedding vectors are created for specific data pairs from
different modalities
(drugs and genes in the example of FIG. 10A) corresponding to different nodes
in the
heterogeneous network. The embedding vectors are created using the data
embedding processes
described above using one-hot vectors and corresponding embedding matrices. In
the example
shown in FIG. 10A, a first embedding vector is created for the drug LAM-002
and a second
embedding vector is created for the gene PIKFYVE. The embedding vectors are
provided as
input to respective drug and gene encoders to map each of the embedding
vectors into a higher-
dimensional modality-specific latent representation in the common latent
representation space.
The architecture of the drug and gene encoders may be similar to those
described above in
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connection with FIG. 7. In some embodiments, the encoder/decoder architecture
may have
different architectures for different modalities by, for example, having a
different number of
hidden layers and/or layers with a different dimensionality, with the output
representation
having the same dimensionality (e.g., 1x95) for each of the encoders/decoders.
In other
embodiments, the architecture for the encoders/decoders is identical for each
modality of data
represented in the statistical model, with the differences between the
encoders/decoders being
reflected in the weights represented in the networks.
[0095] As discussed briefly above, one or both of the encoder/decoder pairs
may be
associated with parameter values that are initialized based on at least one
prior training iteration.
For example, in a scenario in which the intra-modality training of a gene
encoder/decoder as
shown in FIG. 9 was performed prior to the inter-modality training of drug and
gene
encoders/decoders as shown in FIG. 10A, the pre-trained gene encoder/decoder
pair resulting
from the training in FIG. 9 may be used to initialize the parameters of the
gene encoder/decoder
pair in the architecture of FIG. 10A. In this way the encoder/decoder pair for
each modality
continues to be trained as new pairs of data and network interactions are
provided as input to the
multi-modal statistical model.
[0096] As shown in FIG. 10A, the modality-specific latent representations
output from the
encoders may be concatenated to an embedding interaction vector representing a
particular inter-
modality network link between the input data ("binds" in the example of FIG.
10A). In
embodiments in which concatenation is used to incorporate the network link
information in the
common latent representation, the concatenation may occur when generating the
modality-
specific latent representations or the concatenation may occur after the
modality-specific latent
representations have been combined to create a joint representation. The
modality-specific
latent representations may be combined, for example, by taking an average or
product of the two
latent representations to compute a joint representation vector that
represents the "drug-binds-
gene" network interaction for the input data pair of drug LAM-002 and gene
PIKFYVE.
Continuing with the training, the joint representation is provided as input to
both a drug decoder
and a gene decoder to produce decoded output vectors (e.g. having dimension
lx10), and the
parameters of the encoders and decoders (and optionally the embedding
matrices) are updated
based on a comparison of the decoded output vectors and the embedding vectors
provided as
input to the encoders. Examples of how the weights may be updated using
backpropagation in
accordance with some embodiments are discussed above.
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[0097] FIG. 10B shows another example for training a multi-modal
statistical model to learn
inter-modality interactions for a heterogeneous network in accordance with
some embodiments.
In particular, FIG. 10B shows how the statistical model may be trained to
learn the "disease-
associates-gene" network link in the heterogeneous network shown in FIG. 2.
The training
process outlined in FIG. 10B is similar to that described in FIG. 10A, with
the primary
differences being the inputs and the encoders/decoders used for the training.
Briefly, embedding
vectors are created for specific data pairs from different modalities (genes
and diseases in the
example of FIG. 10B) corresponding to different nodes in the heterogeneous
network. The
embedding vectors are created using the data embedding processes described
above using one-
hot vectors and corresponding embedding matrices. In the example shown in FIG.
10B, a first
embedding vector is created for the gene BCL6 and a second embedding vector is
created for the
disease Lymphoma. The embedding vectors are provided as input to respective
gene and
disease encoders to map each of the embedding vectors into a higher-
dimensional modality-
specific latent representation in the common latent representation.
[0098] One or both of the encoder/decoder pairs may be associated with
parameter values
that are initialized based on at least one prior training iteration. For
example, in a scenario in
which the inter-modality training of a gene encoder/decoder as shown in FIG.
10A was
performed prior to the inter-modality training of gene and disease
encoders/decoders in FIG.
10B, the pre-trained gene encoder resulting from the training in FIG. 10A may
be used to
initialize the parameters of the gene encoder and decoder in the architecture
of FIG. 10B. In this
way the encoder/decoder pair for each modality continues to be trained as new
pairs of data and
network interactions are provided as input to the multi-modal statistical
model.
[0099] As shown in FIG. 10B, the modality-specific latent representations
output from the
encoders may be concatenated to an embedding interaction vector representing a
particular inter-
modal network link between the input data ("associates" in the example of FIG.
10B). In
embodiments in which concatenation is used to incorporate the network link
information in the
common latent representation, the concatenation may occur when generating the
modality-
specific latent representations or the concatenation may happen after the
modality-specific latent
representations have been combined to create a joint representation. The
modality-specific
latent representations may be combined, for example, by taking an average or
product of the two
representations to compute a joint representation vector that represents the
"disease-associates-
gene" network interaction for the input data pair of gene BCL6 and disease
Lymphoma.
Continuing with the training, the joint representation is provided as input to
both a gene decoder
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and a disease decoder to produce decoded output vectors (e.g. having dimension
lx10), and the
parameters of the encoders and decoders (and optionally the embedding
matrices) are updated
based on a comparison of the decoded output vectors and the embedding vectors
provided as
input to the encoders. Examples of how the weights may be updated using
backpropagation in
accordance with some embodiments are discussed above.
[00100] FIG. 10C shows another example for training a multi-modal statistical
model to learn
inter-modality interactions for a heterogeneous network in accordance with
some embodiments.
In particular, FIG. 10C shows how the statistical model may be trained to
learn the "drug-treats-
disease" network link in the heterogeneous network shown in FIG. 2. The
training process
outlined in FIG. 10C is similar to that described in FIGS. 10A and 10B, with
the primary
differences being the inputs and the encoders/decoders used for the training.
Briefly, embedding
vectors are created for specific data pairs from different modalities (drugs
and diseases in the
example of FIG. 10C) corresponding to different nodes in the heterogeneous
network. The
embedding vectors are created using the data embedding processes described
above using one-
hot vectors and corresponding embedding matrices. In the example shown in FIG.
10C, a first
embedding vector is created for the drug LAM-002 and a second embedding vector
is created
for the disease Lymphoma. The embedding vectors are provided as input to
respective drug and
disease encoders to map each of the embedding vectors into a higher-
dimensional modality-
specific latent representation in the common latent representation.
[00101] One or both of the encoder/decoder pairs may be associated with
parameter values
that are initialized based on at least one prior training iteration. For
example, in a scenario in
which the inter-modality training of a drug encoder/decoder as shown in FIG.
10A and the inter-
modality training of a disease encoder/decoder in FIG. 10B was performed prior
to the inter-
modality training shown in FIG. 10C, the pre-trained drug encoder/decoder pair
resulting from
the training in FIG. 10A may be used to initialize the parameters of the drug
encoder/decoder
pair in the architecture of FIG. 10C and the pre-trained disease
encoder/decoder pair resulting
from the training in FIG. 10B may be used to initialize the parameters for the
disease
encoder/decoder pair in the architecture of FIG. 10C. In this way the
encoder/decoder pair for
each modality continues to be trained as new pairs of data and network
interactions are provided
as input to the multi-modal statistical model.
[00102] As shown in FIG. 10C, the modality-specific latent representations
output from the
encoders may be concatenated to an embedding interaction vector representing a
particular inter-
modal network link between the input data ("treats" in the example of FIG.
10C). In
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embodiments in which concatenation is used to incorporate the network link
information in the
common latent representation, the concatenation may occur when generating the
modality-
specific latent representations or the concatenation may happen after the
modality-specific latent
representations have been combined to create a joint representation vector.
The modality-
specific latent representations may be combined, for example, by taking an
average or product of
the two representations to compute a joint representation vector that
represents the "drug-treats-
disease" network interaction for the input data pair of drug LAM-002 and
disease Lymphoma.
Continuing with the training, the joint representation vector is provided as
input to both a drug
decoder and a disease decoder to produce decoded output vectors (e.g. having
dimension lx10),
and the parameters of the encoders and decoders (and optionally the embedding
matrices) are
updated based on a comparison of the decoded output vectors and the embedding
vectors
provided as input to the encoders. Examples of how the weights may be updated
using
backpropagation in accordance with some embodiments are discussed above.
[00103] All of the examples provided above in FIGS. 9 and 10A-C relate to
training the
statistical model to learn network interactions in the heterogeneous network
of FIG. 2 that are
categorical only. As discussed above, some network interactions may be both
represented by
both categorical and numerical features, wherein the numerical features
represent a strength of
an interaction between data within or among nodes in the network. For training
the multi-modal
statistical modal to learn network links that are both categorical and
numerical, the numerical
information may be used to scale the representation vectors represented in the
joint-modality
representation. For example, the embedding interaction vectors concatenated to
the joint
representation vectors may be scaled by the numerical information.
[00104] Various parameters (e.g., hyperparameters) of the multi-modal
statistical architecture
may be modified based on optimization for a particular implementation. Such
parameters
include but, are not limited to, embedding dimension (example, lx10), joint
representation
dimension (example, lx100), dimension of hidden layer(s) of encoders and
decoder (example,
1x50), number of hidden layers of encoders and decoders (example, 1),
activation function for
the encoders and decoders, and the learning rate.
[00105] As discussed in connection with FIG. 3, the overall architecture of
the multi-modal
statistical model once trained includes a plurality of trained modality-
specific encoders and
decoders and a joint-modality representation that couples the trained encoders
to the trained
decoders. As shown schematically in FIG. 11, the trained multi-modal
statistical model may be
used to make predictions between input data having a first modality and an
output having a
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different modality through the selection of an appropriate pair of trained
encoders and decoders
used for the prediction. Specifically, FIG. 11 shows the ability of the
trained multi-modal
statistical model to make predictions about diseases that are likely be
treatable by a particular
drug. The prediction is made, in part, by using a trained drug encoder and a
trained disease
decoder, as shown. Multiple types of predictions can be made using the trained
multi-modal
statistical network, including, but not limited to, new disease indications
for a given drug,
candidate drugs and combination therapies for a given disease or patient,
biomarkers associated
with a disease, and potential gene targets for a given drug. Making such
predictions is not
possible using conventional techniques for modeling biological data that
consider only one or
two modalities of data.
[00106] Some embodiments are directed to unsupervised prediction techniques
using a
trained multi-modal statistical model. FIG. 12 shows an example of an
unsupervised prediction
technique in which the representation space for a first modality (drug in the
example of FIG. 12)
is mapped onto the representation space for a second modality (disease in the
example of FIG.
12) using a decoder for the second modality. In the prediction technique shown
in FIG. 12,
candidate disease indications are predicted for a given drug provided as input
to the trained
statistical model. The trained drug encoder is used to compute a latent
representation vector for
the drug of interest in the joint-modality representation, and the latent
representation vector is
provided as input to the trained disease decoder. The output of the trained
disease decoder is a
representation of the input drug projected into the disease representation
space.
[00107] Rather than mapping the input drug to a particular disease in the
disease
representation space, the output of the disease decoder may be projected as a
point 1310 in the
disease representation space, as shown schematically in FIG. 13. The disease
representation
space shown in FIG. 13 is a t-Distributed Stochastic Neighbor Embedding (t-
SNE)
representation of the "disease latent space" containing just a subset of the
disease database.
Each of the diseases on which the multi-modal statistical model was trained
also has an intrinsic
position in the n-dimensional disease representation space. In some
embodiments, a new disease
indication is predicted based, at least in part, on a distance between the
projected point 1310 and
the positions of other diseases in the disease representational space. For
example, new disease
indications for the drug may be determined by finding nearest neighbors of the
projected point
1310 and candidate diseases projected within the disease representation space.
Candidate
diseases with the highest potential of being treatable by the given drug may
include diseases in
which the distance between the project point 1310 and the points for the
candidate diseases is
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small. For example, as shown in FIG. 13, the diseases of gout, migraine and
multiple sclerosis
are each associated with points in the disease representation space closest to
the projected point
1310 for a given input drug. As such, these diseases may be good candidates as
new disease
targets for the drug of interest.
[00108] In some embodiments, only the disease having the closest distance to
the projected
point 1310 may be provided as an output prediction. In other embodiments, an
"n-best" list of
diseases associated with distances closest to the projected point 1310 may be
provided as an
output prediction. In yet other embodiments, only diseases having a distance
less than a
threshold value from the projected point 1310 in the disease representation
space may be output.
Other information in addition to the disease name(s) may be output including,
but not limited to,
a similarity score based on the distance.
[00109] Any suitable measure of distance between two points in the n-
dimensional
representation space may be used, and embodiments are not limited in this
respect. Examples of
distance measurements that can be used in accordance with some embodiments for
prediction
include, but are not limited to, Euclidean distance, Cosine similarity, and
Manhattan distance. A
formula for Euclidean distance between two vectors in a common representation
space may be
as follows:
d(131(1) d(cliP) 1(q1 PO- -- -- (r/2 .. p2)2 ((in
PrI)2
[00110] FIG. 14 shows an example of another unsupervised prediction technique
in which
input data for two different modalities (drug and disease in the example of
FIG. 14) is projected
into the joint-modality representation space, where comparisons between the
joint representation
vectors from the different modalities can be made. As shown, in the prediction
technique of
FIG. 14, input data for a first modality (drug in the example of FIG. 14) is
provided to a trained
encoder for the first modality. The output of the trained encoder for the
first modality is a first
joint representation vector for the first modality input in the common latent
space. Additionally,
input data for a second modality (a plurality of diseases in the example of
FIG. 14) are provided
as input to a trained encoder for the second modality. The output of the
trained encoder for the
second modality is a plurality of second joint representation vectors
represented in the common
latent space, each of which corresponds to input data for the second modality.
[00111] A prediction for candidate disease indications for a given drug may be
determined by
comparing a distance of the first joint representation vector for the input
drug within the
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common latent space and each of the second joint representation vectors for
the projected
diseases into the common latent space. For example, in order to predict the
association between
a drug A and four different diseases, the drug and disease encoders may be
used to compute the
corresponding latent representations for drug A and each of the four diseases.
The distance
between the latent representation vectors for drug A and those for each
disease projected into the
common latent space may be computed to predict the closest disease
representation to the
representation of drug A. The candidate diseases with the highest potential of
being treatable by
the given drug may be those diseases having positions in the latent
representation space that are
closest to the position of the drug of interest in the latent representation
space.
[00112] Although the unsupervised prediction techniques described in FIGS. 12
and 14 relate
to predicting new disease indications for particular drugs, it should be
appreciated that
unsupervised prediction techniques may be used to make predictions between any
two
modalities represented in the trained statistical model by selecting
appropriate trained encoders
and/or decoders to enable the prediction within a common representation space
within the multi-
modal statistical model.
[00113] Some embodiments are directed to supervised prediction techniques
using a trained
multi-modal statistical model. FIG. 15 illustrates a supervised prediction
technique that uses a
supervised classifier trained with known network interactions of two different
modalities. The
supervised classifier may be implemented using any suitable architecture
including, but not
limited to, a neural network, a tree-base classifier, other deep learning or
machine learning
classifiers, or using statistical correlation techniques. The classifier may
be trained with the
latent representations of the known network interaction pairs (e.g., from
approved disease
indications for FDA approved drugs), and predictions about whether or not
there is a true
association given new pair may be made using the trained classifier.
[00114] As shown, the supervised classifier in FIG. 15 may be trained with
representation
vectors of FDA-approved drug-disease pairs. The input vectors for drugs and
diseases may have
a dimension corresponding to a data embeddings layer (e.g., lx10) if using the
disease decoder
to project the drugs to the disease representation space or the drug decoder
to project the
diseases to the drug representation space, or a dimension of the latent
representation space (e.g.,
1x95) if using the latent representation of both modalities to make
classification decisions using
the trained supervised classifier.
[00115] In addition to the predication examples described above, other types
of predictions
are also contemplated by some embodiments. For example, predictions about new
drugs that
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may be effective in treating a given disease may be made. A disease of
interest and all drugs
may be projected into a common representation space (e.g., a modality-specific
representation
space or the common latent space) in the multi-modal statistical model and
distances between
vectors in the common representation space may be used to predict the new
drugs for treating
the disease.
[00116] Because all entities in the heterogeneous network represented in the
multi-modal
statistical model have representations in the same latent space, and encoders
and decoders have
been trained to access the latent space, other cross-modality predictions, in
addition to new drug-
disease matches, can be made. For example, diseases can be encoded by a
trained disease
encoder to predict gene targets in the common latent space, or by passing the
disease latent
representation through the gene decoder and comparing the representation
directly in the gene
space (e.g., through nearest neighbor and other aforementioned distance
measurement or
similarity techniques). In this manner, in addition to predicting new drugs
associated with a
given disease, genes, proteins, pathways, anatomies, and other biological
entities can be also be
associated with the disease, providing context to the drug-disease prediction.
Additionally, a
specific mutation in the heterogeneous network can be shown to have strong
associations with
drugs and diseases, thereby indicating biomarkers that could help to identify
patients that will
respond to given drugs.
[00117] In yet another prediction scenario, gene targets of a drug may be
predicted in
accordance with some embodiments. Drugs are associated with genes, mutations,
and other
heterogeneous network entities, which may provide mechanistic insights of drug
action. This
can be valuable, for example, for further fine-tuning of drug-disease
predictions based on expert
knowledge and traditional drug engineering.
[00118] Yet another prediction technique relates to predicting patient-
specific therapies. The
trained multi-modal statistical model may be used to predict specific
drugs/therapies for specific
patients. For example, as described above some embodiments are configured to
predict
biomarkers associated with a given disease. Patients can be screened for these
biomarkers, and
patients harboring these biomarkers may be predicted to be good candidates for
treatment by the
given drug.
[00119] As described above, additional modalities not illustrated in FIG. 2
may also be added
to the heterogeneous network represented by a multi-modal statistical network
trained in
accordance with the techniques described herein. One such modality that may be
added relates
to patients. For example, patient information may be included in the
heterogeneous network
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through proximity of their patients' properties (e.g., gene expression,
mutation, copy number
variation, DNA methylation) to other entities in the heterogeneous network, or
by defining a
patient entity as a new node in the heterogeneous network (e.g., with a single
patient encoder
and decoder used for projecting patient information to the common latent
space).
[00120] In the former scenario, patients are represented in the multi-modal
statistical model
based on their gene expression profiles (or other experimentally procured
attributes), and this
information may be linked to other nodes (such as by proximity to known
expression profiles of
drugs and diseases), and the linked nodes may be used for projection into the
latent space.
[00121] In the latter scenario, a new patient entity or node may be added to
the heterogeneous
network, with its own encoder and decoder included in the multi-modal
statistical model.
Network links in the heterogeneous network may be formed between individual
patients
(represented by a patient node) and the drug and disease nodes in the network,
for example,
based on patients known to react well to particular drugs or to harbor
diseases. Furthermore,
links in the heterogeneous network may be formed between two patients that
harbor similar gene
expression profiles or other experimentally procured biological information or
attributes (e.g.,
DNA, RNA, Protein, medical imaging). The patient encoder and decoder may be
trained in a
similar manner as encoder/decoder pairs for other nodes in the heterogeneous
network, as
described above. Predictions using the trained patient encoder/decoder may be
made, for
example, between a patient of interest and a candidate drug, using one or more
of the techniques
described herein.
[00122] An illustrative implementation of a computer system 1600 that may be
used in
connection with any of the embodiments of the disclosure provided herein is
shown in FIG. 16.
The computer system 1600 may include one or more computer hardware processors
1600 and
one or more articles of manufacture that comprise non-transitory computer-
readable storage
media (e.g., memory 1620 and one or more non-volatile storage devices 1630).
The processor
1610(s) may control writing data to and reading data from the memory 1620 and
the non-volatile
storage device(s) 1630 in any suitable manner. To perform any of the
functionality described
herein, the processor(s) 1610 may execute one or more processor-executable
instructions stored
in one or more non-transitory computer-readable storage media (e.g., the
memory 1620), which
may serve as non-transitory computer-readable storage media storing processor-
executable
instructions for execution by the processor(s) 1610.
[00123] The terms "program" or "software" are used herein in a generic sense
to refer to any
type of computer code or set of processor-executable instructions that can be
employed to
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program a computer or other processor (physical or virtual) to implement
various aspects of
embodiments as discussed above. Additionally, according to one aspect, one or
more computer
programs that when executed perform methods of the disclosure provided herein
need not reside
on a single computer or processor, but may be distributed in a modular fashion
among different
computers or processors to implement various aspects of the disclosure
provided herein.
[00124] Processor-executable instructions may be in many forms, such as
program modules,
executed by one or more computers or other devices. Generally, program modules
include
routines, programs, objects, components, data structures, etc. that perform
particular tasks or
implement particular abstract data types. Typically, the functionality of the
program modules
may be combined or distributed.
[00125] Also, data structures may be stored in one or more non-transitory
computer-readable
storage media in any suitable form. For simplicity of illustration, data
structures may be shown
to have fields that are related through location in the data structure. Such
relationships may
likewise be achieved by assigning storage for the fields with locations in a
non-transitory
computer-readable medium that convey relationship between the fields. However,
any suitable
mechanism may be used to establish relationships among information in fields
of a data
structure, including through the use of pointers, tags or other mechanisms
that establish
relationships among data elements.
[00126] Various inventive concepts may be embodied as one or more processes,
of which
examples have been provided. The acts performed as part of each process may be
ordered in
any suitable way. Thus, embodiments may be constructed in which acts are
performed in an
order different than illustrated, which may include performing some acts
simultaneously, even
though shown as sequential acts in illustrative embodiments.
[00127] As used herein in the specification and in the claims, the phrase "at
least one," in
reference to a list of one or more elements, should be understood to mean at
least one element
selected from any one or more of the elements in the list of elements, but not
necessarily
including at least one of each and every element specifically listed within
the list of elements
and not excluding any combinations of elements in the list of elements. This
definition also
allows that elements may optionally be present other than the elements
specifically identified
within the list of elements to which the phrase "at least one" refers, whether
related or unrelated
to those elements specifically identified. Thus, for example, "at least one of
A and B" (or,
equivalently, "at least one of A or B," or, equivalently "at least one of A
and/or B") can refer, in
one embodiment, to at least one, optionally including more than one, A, with
no B present (and
CA 03100065 2020-11-12
WO 2019/231624 PCT/US2019/031260
optionally including elements other than B); in another embodiment, to at
least one, optionally
including more than one, B, with no A present (and optionally including
elements other than A);
in yet another embodiment, to at least one, optionally including more than
one, A, and at least
one, optionally including more than one, B (and optionally including other
elements);etc.
[00128] The phrase "and/or," as used herein in the specification and in the
claims, should be
understood to mean "either or both" of the elements so conjoined, i.e.,
elements that are
conjunctively present in some cases and disjunctively present in other cases.
Multiple elements
listed with "and/or" should be construed in the same fashion, i.e., "one or
more" of the elements
so conjoined. Other elements may optionally be present other than the elements
specifically
identified by the "and/or" clause, whether related or unrelated to those
elements specifically
identified. Thus, as a non-limiting example, a reference to "A and/or B", when
used in
conjunction with open-ended language such as "comprising" can refer, in one
embodiment, to A
only (optionally including elements other than B); in another embodiment, to B
only (optionally
including elements other than A); in yet another embodiment, to both A and B
(optionally
including other elements); etc.
[00129] Use of ordinal terms such as "first," "second," "third," etc., in the
claims to modify a
claim element does not by itself connote any priority, precedence, or order of
one claim element
over another or the temporal order in which acts of a method are performed.
Such terms are
used merely as labels to distinguish one claim element having a certain name
from another
element having a same name (but for use of the ordinal term). The phraseology
and terminology
used herein is for the purpose of description and should not be regarded as
limiting. The use of
"including," "comprising," "having," "containing", "involving", and variations
thereof, is meant
to encompass the items listed thereafter and additional items.
[00130] Having described several embodiments of the techniques described
herein in detail,
various modifications, and improvements will readily occur to those skilled in
the art. Such
modifications and improvements are intended to be within the spirit and scope
of the disclosure.
Accordingly, the foregoing description is by way of example only, and is not
intended as
limiting. The techniques are limited only as defined by the following claims
and the equivalents
thereto.
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