# Graph Representation Learning in Biomedicine

Michelle M. Li  Bioinformatics and Integrative Genomics Program, Harvard
Medical School, Boston, MA 02115, USA Department of Biomedical Informatics,
Harvard Medical School, Boston, MA 02115, USA  Kexin Huang  Health Data
Science Program, Harvard T.H. Chan School of Public Health, Boston, MA 02115,
USA  Marinka Zitnik  Department of Biomedical Informatics, Harvard Medical
School, Boston, MA 02115, USA  Broad Institute of MIT and Harvard, Cambridge,
MA 02142, USA  Harvard Data Science Initiative, Cambridge, MA 02138, USA
Correspondence<EMAIL_ADDRESS>
###### Abstract

Biomedical networks (or graphs) are universal descriptors for systems of
interacting elements, from molecular interactions and disease co-morbidity to
healthcare systems and scientific knowledge. Advances in artificial
intelligence, specifically deep learning, have enabled us to model, analyze,
and learn with such networked data. In this review, we put forward an
observation that long-standing principles of systems biology and
medicine—while often unspoken in machine learning research—provide the
conceptual grounding for representation learning on graphs, explain its
current successes and limitations, and even inform future advancements. We
synthesize a spectrum of algorithmic approaches that, at their core, leverage
graph topology to embed networks into compact vector spaces. We also capture
the breadth of ways in which representation learning has dramatically improved
the state-of-the-art in biomedical machine learning. Exemplary domains covered
include identifying variants underlying complex traits, disentangling
behaviors of single cells and their effects on health, assisting in diagnosis
and treatment of patients, and developing safe and effective medicines.

##  1 Introduction

Networks (or graphs) are pervasive in biology and medicine, from molecular
interaction maps to population-scale social and health interactions. With the
multitude of bioentities and associations that can be described by networks,
they are prevailing representations of biological organization and biomedical
knowledge. For instance, edges in a regulatory network can indicate causal
activating and inhibitory relationships between genes [149]; edges between
genes and diseases can indicate genes that are ‘upregulated by’,
‘downregulated by’, or ‘associated with’ a disease [141]; and edges in a
knowledge network built from electronic health records (EHR) can indicate co-
occurrences of medical codes across patients [156, 161, 81]. The ability to
model all biomedical discoveries to date—even overlay patient-specific
information—in a unified data representation has driven the development of
artificial intelligence, specifically deep learning, for networks. In fact,
the diversity and multimodality in networks not only boost performance of
predictive models, but importantly enable broad generalization to settings not
seen during training [74] and improve model interpretability [140, 31].
Nevertheless, interactions in networks give rise to a bewildering degree of
complexity that can likely only be fully understood through a holistic and
integrated view [22, 14, 137]. As a result, systems biology and medicine—upon
which deep learning on graphs is founded—have identified over the last two
decades organizing principles that govern networks [66, 13, 85, 227].

Figure 1: Representation learning for networks in biology and medicine. Given
a biomedical network, a representation learning method transforms the graph to
extract patterns and leverage them to produce compact vector representations
that can be optimized for the downstream task. The far right panel shows a
local 2-hop neighborhood around node $u$, illustrating how information (e.g.,
neural messages) can be propagated along edges in the neighborhood,
transformed, and finally aggregated at node $u$ to arrive at the $u$’s
embedding.

The organizing principles governing networks link network structure to
molecular phenotypes, biological roles, disease, and health, thus providing
the conceptual grounding that, we argue, can explain the successes (and
limitations) of graph representation learning and inform future development of
the field. Here, we exemplify how a series of such principles has uncovered
disease mechanisms. First, interacting entities are typically more similar
than non-interacting entities, as implicated by the local hypothesis [13]. In
protein interaction networks, for instance, mutations in interacting proteins
often lead to similar diseases [13]. Given by the shared components and
disease module hypotheses [13], cellular components associated with the same
phenotype tend to cluster in the same network neighborhood [4]. Further,
essential genes are often found in hubs of a molecular network whereas non-
essential genes (e.g., those associated with disease) are located on the
periphery [13]. Thus, the network parsimony principle dictates that shortest
molecular paths between known disease-associated components tend to correlate
with causal molecular pathways [13]. To this day, these hypotheses and
principles continue to drive discoveries.

We posit that representation learning can realize network biomedicine
principles. Its core idea is to learn how to represent nodes (or larger graph
structures) in a network as points in a low-dimensional space, where the
geometry of this space is optimized to reflect the structure of interactions
between nodes. More concretely, representation learning specifies deep, non-
linear transformation functions that map nodes to points in a compact vector
space, termed embeddings. Such functions are optimized to embed the input
network so that nodes with similar network neighborhoods are embedded close
together in the embedding space, and algebraic operations performed in this
learned space reflect the network’s topology. To provide concrete connections
between graph representation learning and systems biology and medicine: nodes
in the same positional regions should have similar embeddings due to the local
hypothesis (e.g., highly similar pairs of protein embeddings suggest similar
phenotypic consequence); node embeddings can capture whether the nodes lie
within a hub based on their degree, an important aspect of local neighborhood
(e.g., strongly clustered gene embeddings indicate essential housekeeping
roles); and given by the shared components hypothesis, two nodes with
significantly overlapping sets of network neighbors should have similar
embeddings due to shared message passing (e.g., highly similar disease
embeddings imply shared disease-associated cellular components). Hence,
artificial intelligence methods that produce representations can be thought of
as differentiable engines of key network biomedicine principles.

Our survey provides an exposition of graph artificial intelligence capability
and highlights important applications for deep learning on biomedical
networks. Given the prominence of graph representation learning, specific
aspects of it have been covered extensively. However, existing reviews
independently discuss deep learning on structured data [23, 220]; graph neural
networks [76, 75, 195]; representation learning for homogeneous and
heterogeneous graphs [27, 112, 214], solely heterogeneous graphs [52], and
dynamic graphs [99]; data fusion [229]; network propagation [44]; topological
data analysis [16]; and creation of biomedical networks [16, 22, 106, 118,
151]. Biomedically-focused reviews survey graph neural networks exclusively on
molecular generation [46, 191], single-cell biology [80], drug discovery and
repurposing [96, 170, 63, 126, 217], or histopathology [5]. Other reviews tend
to focus solely on graph neural networks, excluding other graph representation
learning approaches or do not consider patient-centric methods [138]. In
contrast, our survey unifies graph representation learning approaches across
molecular, genomic, therapeutic, and precision medicine areas.

##  2 Graph representation learning

Graph theoretic techniques have fueled discoveries, from uncovering
relationships between diseases [70, 109, 133, 169] to repurposing drugs [34,
35, 74]. Further algorithmic innovations, such as random walks [32, 192, 205],
kernels [64], and network propagation [181], have also played a role in
capturing structural information in networks. Feature engineering, the process
of extracting predetermined features from a network to suit a user-specified
machine learning method [222], is a common approach for machine learning on
networks, including but not limited to hard-coding network features (e.g.,
higher-order structures, network motifs, degree counts, and common neighbor
statistics) and feeding the engineered feature vectors into a machine learning
model. While powerful, it can be challenging to hand engineer optimally-
predictive features across diverse types of networks and applications [220].

For these reasons, graph representation learning, the idea of automatically
learning optimal features for networks, has emerged as a leading artificial
intelligence approach for networks. Graph representation learning is
challenging because graphs contain complex topographical structure, have no
fixed node ordering or reference points, and are comprised of many different
kinds of entities (nodes) and various types of interactions (edges) relating
them to each other. Classic deep learning methods are unable to consider such
diverse structural properties and rich interactions, which are the essence of
biomedical networks, because classic deep methods are designed for fixed-size
grids (e.g., images and tabular datasets) or optimized for text and sequences.
Akin to how deep learning on images and sequences has revolutionized image
analysis and natural language processing, graph representation learning is
poised to transform the study of complex systems.

Graph representation learning methods generate vector representations for
graph elements such that the learned representations, i.e., embeddings,
capture the structure and semantics of networks, along with any downstream
supervised task, if any (Box 2). Graph representation learning encompasses a
wide range of methods, including manifold learning, topological data analysis,
graph neural networks and generative graph models (Figure 2). We next describe
graph elements and outline main artificial intelligence tasks on graphs (Box
2). We then outline graph representation learning methods (Section 2.1-2.3).

Fundamentals of graph representation learning

Elements of graphs. Graph $G=(\mathcal{V},\mathcal{E})$ consists of nodes
$v\in\mathcal{V}$ and edges or relations $e^{r}_{u,v}\in\mathcal{E}$
connecting nodes $u$ and $v$ via a relationship of type $r$. Subgraph
$S=(\mathcal{V}_{S},\mathcal{E}_{S})$ is a subset of a graph $G$, where
$\mathcal{V}_{S}\subseteq\mathcal{V}$ and
$\mathcal{E}_{S}\subseteq\mathcal{E}$. Adjacency matrix $\mathbf{A}$ is used
to represent a graph, where each entry $\mathbf{A}_{u,v}$ is 1 if nodes $u,v$
are connected, and 0 otherwise. $\mathbf{A}_{u,v}$ can also be the edge weight
between nodes $u,v$. Homogeneous graph is a graph with a single node and edge
type. In contrast, heterogeneous graph consists of nodes of different types
(node type set $\mathcal{A}$) connected by diverse kinds of edges (edge type
set $\mathcal{R}$). Node attribute vector $\mathbf{x}_{u}\in\mathbb{R}^{d}$
describes side information and metadata of node $u$. The node attribute matrix
$\mathbf{X}\in\mathbb{R}^{n\times d}$ brings together attribute vectors for
all nodes in the graph. Similarly, edge attributes
$\mathbf{x}_{u,v}^{e}\in\mathbb{R}^{c}$ for edge $e_{u,v}$ can be taken
together to form an edge attribute matrix
$\mathbf{X}^{e}\in\mathbb{R}^{m\times c}$. A path from node $u_{1}$ to node
$u_{k}$ is given by a sequence of edges $u_{1}\xrightarrow{e_{1,2}}u_{2}\cdots
u_{k-1}\xrightarrow{e_{k-1,k}}u_{k}$. For node $u$, we denote its neighborhood
$\mathcal{N}(u)$ as nodes directly connected to $u$ in $G$, and the node
degree is the size of $\mathcal{N}(u)$. The $k$-hop neighborhood of node $u$
is the set of nodes that are exactly $k$ hops away from node $u$, that is,
$\mathcal{N}^{k}(u)=\\{v|d(u,v)=k\\}$ where $d$ denotes the shortest path
distance (SI Note 1).

Artificial intelligence tasks on graphs. To extract this information from
networks, classic machine learning approaches rely on summary statistics
(e.g., degrees or clustering coefficients) or carefully engineered features to
measure network structures (e.g., network motifs). In contrast, representation
learning approaches automatically learn to encode networks into low-
dimensional representations (i.e., embeddings) using transformation techniques
based on deep learning and nonlinear dimensionality reduction. The flexibility
of learned representations shows in a myriad of tasks that representations can
be used for (SI Note 2):

  * •

Node, link, and graph property prediction: The objective is to learn
representations of graph elements, such as nodes, edges, subgraphs, and entire
graphs. Representations are optimized so that performing algebraic operations
in the embedding space reflects the graph’s topology. Optimized
representations can be fed into models to predict properties of graph
elements, such as the function of proteins in an interactome network (i.e.,
node classification task), the binding affinity of a chemical compound to a
target protein (i.e., link prediction task), and the toxicity profile of a
candidate drug (i.e., graph classification task).

  * •

Latent graph learning: Graph representation learning exploits relational
inductive biases for data that come in the form of graphs. In some settings,
however, the graphs are not readily available for learning. This is typical
for many biological problems, where graphs such as gene regulatory networks
are only partially known. Latent graph learning is concerned with inferring
the graph from the data. The latent graph can be application-specific and
optimized for the downstream task. Further, such a graph might be as important
as the task itself, as it can convey insights about the data and offer a way
to interpret the results.

  * •

Graph generation: The objective is to generate a graph $G$ representing a
biomedical entity that is likely to have a property of interest, such as high
druglikeness. The model is given a set of graphs $\mathcal{G}$ with such a
property and is tasked with learning a non-linear mapping function
characterizing the distribution of graphs in $\mathcal{G}$. The learned
distribution is used to optimize a new graph $G$ with the same property as
input graphs.

Figure 2: Predominant paradigms in graph representation learning. (a) Shallow
network embedding methods generate a dictionary of representations
$\mathbf{h}_{u}$ for every node $u$ that preserves the input graph structure
information. This is achieved by learning a mapping function $f_{z}$ that maps
nodes into an embedding space such that nodes with similar graph neighborhoods
measured by function $f_{n}$ get embedded closer together (Section 2.1). Given
the learned embeddings, an independent decoder method can optimize embeddings
for downstream tasks, such as node or link property prediction. Method
examples include DeepWalk [148], Node2vec [68], LINE [173], and Metapath2vec
[51]. (b) In contrast with shallow network embedding methods, graph neural
networks can generate representations for any graph element by capturing both
network structure and node attributes and metadata. The embeddings are
generated through a series of non-linear transformations, i.e., message-
passing layers ($L_{k}$ denotes transformations at layer $k$), that
iteratively aggregate information from neighboring nodes at the target node
$u$. GNN models can be optimized for performance on a variety of downstream
tasks (Section 2.2). Method examples include GCN [103], GIN [198], GAT [180],
and JK-Net [200]. (c) Generative graph models estimate a distribution
landscape $\mathbf{Z}$ to characterize a collection of distinct input graphs.
They use the optimized distribution to generate novel graphs $\widehat{G}$
that are predicted to have desirable properties, e.g., a generated graph can
be represent a molecular graph of a drug candidate. Generative graph models
use graph neural networks as encoders and produce graph representations that
capture both network structure and attributes (Section 2.3). Method examples
include GCPN [210], JT-VAE [98], and GraphRNN [211]. SI Figure 1 and SI Note 3
outline other representation learning techniques.

###  2.1 Shallow graph embedding approaches

Shallow embedding methods optimize a compact vector space such that points
that are close in the graph are mapped to nearby points in the embedding
space, which is measured by a predefined distance function or an outer
product. These approaches are transductive embedding methods where the encoder
function is a simple embedding lookup (Figure 2). More concretely, the methods
have three steps: (1) Mapping to an embedding space. Given a pair of nodes $u$
and $v$ in graph $G$, we specify an encoder, a learnable function $f$ that
maps nodes to embeddings $\mathbf{h}_{u}$ and $\mathbf{h}_{v}$. (2) Defining
graph similarity. We define the graph similarity $f_{n}(u,v)$, for example,
measured by distance between $u$ and $v$ in the graph, and the embedding
similarity $f_{z}(\mathbf{h}_{u},\mathbf{h}_{v})$, for example, an Euclidean
distance function or pairwise dot-product. (3) Computing loss. Then, we define
the loss $\mathcal{L}(f_{n}(u,v),f_{z}(\mathbf{h}_{u},\mathbf{h}_{v}))$, which
quantifies how the resulting embeddings preserve the desired input graph
similarity. Finally, we apply an optimization procedure to minimize the loss
$\mathcal{L}(f_{n}(u,v),f_{z}(\mathbf{h}_{u},\mathbf{h}_{v}))$. The resulting
encoder $f$ is a graph embedding method that serves as a shallow embedding
lookup and considers the graph structure only in the loss function.

Shallow embedding methods vary given various definitions of similarities. For
example, shortest path length between nodes is often used as the network
similarity and dot product as the embedding similarity. Perozzi et al. [148]
define similarity as co-occurrence in a series of random walks of length $k$.
Unsupervised techniques that predict which node belongs to the walk, such as
skip-gram [134], are then applied on the walks to generate embeddings. Grover
et al. [68] propose an alternative way for walks on graphs, using a
combination of depth-first search and breadth-first search. In heterogeneous
graphs, information on the semantic meaning of edges, i.e., relation types,
can be important. Knowledge graph methods expand similarity measures to
consider relation types [17, 142, 177, 171, 202, 51]. When shallow embedding
models are trained, the resulting embeddings can be fed into separate models
to be optimized towards a specific classification or regression task.

###  2.2 Graph neural networks

Graph neural networks (GNNs) are a class of neural networks designed for
graph-structured datasets (Figure 2). They learn compact representations of
graph elements, their attributes, and supervised labels if any. A typical GNN
consists of a series of propagation layers [65], where layer $l$ carries out
three operations: (1) Passing neural messages. The GNN computes a message
$\mathbf{m}^{(l)}_{u,v}=\textsc{Msg}(\mathbf{h}_{u}^{(l-1)},\mathbf{h}_{v}^{(l-1)})$
for every linked nodes $u,v$ based on their embeddings from the previous layer
$\mathbf{h}_{u}^{(l-1)}$ and $\mathbf{h}_{v}^{(l-1)}$. (2) Aggregating
neighborhoods. The messages between node $u$ and its neighbors
$\mathcal{N}_{u}$ are aggregated as
$\widehat{\mathbf{m}}^{(l)}_{u}=\textsc{Agg}({\mathbf{m}^{(l)}_{uv}|v\in\mathcal{N}_{u}})$.
(3) Updating representations. Non-linear transformation is applied to update
node embeddings as
$\mathbf{h}^{(l)}_{u}=\textsc{Upd}(\widehat{\mathbf{m}}^{(l)}_{u},\mathbf{h}^{(l-1)}_{u})$
using the aggregated message and the embedding from the previous layer. In
contrast to shallow embeddings, GNNs can capture higher-order and non-linear
patterns through multi-hop propagation within several layers of neural message
passing. Additionally, GNNs can optimize supervised signals and graph
structure simultaneously, whereas shallow embedding approaches require a two-
stage approach to achieve the same.

A myriad of GNN architectures define different messages, aggregation, and
update schemes to derive deep graph embeddings [54, 183, 47, 103]. For
example, [180, 87, 215, 201, 39] assign importance scores for nodes during
neighborhood aggregation such that more important nodes play a larger effect
in the embeddings. [199, 1] improve GNNs’ ability to capture graph structural
information by posing structural priors, such as a higher-order adjacency
matrix. Graph pooling techniques [209] learn abstract topological structures.
GNNs designed for molecules [162, 104] inject physics-based scores and domain
knowledge into propagation layers.

As biomedical networks can be multimodal and massive, special consideration is
needed to scale GNNs to large and heterogeneous networks. To this end, [37,
216] developed sampling strategies to intelligently select small subsets of
the whole local network neighborhoods and use them in training GNN models. To
tackle heterogeneous relations, [160, 189, 87] designed aggregation
transformations to fuse diverse types of relations and attributes. Recent
architectures describe dynamic message passing [145, 158, 87] to deal with
evolving and time-varying graphs and few-shot learning [91] or self-supervised
strategies [86, 212] to deal with graphs that are poorly annotated and have
limited label information.

###  2.3 Generative graph models

Generative graph models generate new node and edge structures—even entire
graphs—that are likely to have desired properties, such as novel molecules
with acceptable toxicity profiles (Figure 2). Traditionally, network science
models can generate graphs using deterministic or probabilistic rules. For
instance, the Erdös-Rényi model [57] keeps iteratively adding random edges
according to a predefined probability starting from an empty graph; the
Barabási-Albert model [7] grows a graph by adding nodes and edges such that
the resulting graph has a power-law degree distribution often observed in
real-world networks; the configuration model [12] adds edges based on a
predefined node degree sequences to generate graphs with arbitrary degree
distributions. While powerful as random graph generators, such models cannot
optimize graph structure based on properties of interest.

Deep generative models address the challenge by estimating distributional
graph properties based on a dataset of graphs $\mathcal{G}$ and inferring
graph structures using such optimized distributions. In particular, a
generation graph model first learns a latent distribution $P(Z|\mathcal{G})$
that characterizes the input graph set $\mathcal{G}$. Then, conditioned on
this distribution, it decodes a new graph, i.e., generates a new graph
$\widehat{G}$. There are different ways to encode the input graphs and learn
the latent distribution, such as through variational autoencoders [102, 67,
98] and generative adversarial networks [185]. Decoding to generate a novel
graph presents a unique challenge compared to an image or text since a graph
is discrete, is unbounded in structure and size, and has no particular
ordering of nodes. Common practices to generate new graphs include (1)
predicting a probabilistic fully-connected graph and then using graph matching
to find the optimal subgraph [164]; (2) decomposing a graph into a tree of
subgraphs structure and generating a tree structure instead, followed by
assembles of subgraphs) [98]; and (3) sequentially sampling new nodes and
edges [211, 210].

##  3 Application areas in biology and medicine

Figure 3: Overview of biomedical applications areas. Networks are prevalent
across biomedical areas, from the molecular level to the healthcare systems
level. Protein structures and therapeutic compounds can be modeled as a
network where nodes represent atoms and edges indicate a bond between pairs of
atoms. Protein interaction networks contain nodes that represent proteins and
edges that indicate physical interactions (top left). Drug interaction
networks are comprised of drug nodes connected by synergistic or antagonistic
relationships (bottom left). Protein- and drug-interaction networks can be
combined using an edge type that signifies a protein being a “target" of a
drug (left). Disease association networks often contain disease nodes with
edges representing co-morbidity (middle). Edges exist between proteins and
diseases to indicate proteins (or genes) associated with a disease (top
middle). Edges exist between drugs and diseases to signify drugs that are
indicated for a disease (bottom middle). Patient-specific data, such as
medical images (e.g., spatial networks of cells, tumors, and lymph nodes) and
EHRs (e.g., networks of medical codes and concepts generated by co-occurrences
in patients’ records), are often integrated into a cross-domain knowledge
graph of proteins, drugs, and diseases (right). With such vast and diverse
biomedical networks, we can derive fundamental insights about biology and
medicine while enabling personalized representations of patients for precision
medicine. Note that there are many other types of edge relations; “targets,"
“is associated with," “is indicated for," and “has phenotype" are a few
examples.

Biomedical data involve rich multimodal and heterogeneous interactions that
span from molecular to societal levels (Figure 3). Unlike machine learning
approaches designed to analyze data modalities like medical images and
biological sequences, graph representation learning methods are uniquely able
to leverage structural information in multimodal datasets [204].

Starting at the molecular level (Section 4), molecular structure is translated
from atoms and bonds into nodes and edges, respectively. Physical interactions
or functional relationships between proteins also naturally form a network.
Given by the key organizing principles that govern network medicine—for
instance, the local hypothesis and the shared components hypothesis
[13]—whether an unknown protein clusters in a particular neighborhood of and
shares direct neighbors with known proteins are informative of its binding
affinity, function, etc [90]. Grounded in network medicine principles, graph
machine learning is commonly applied to learn molecular representations of
proteins and their physical interactions for predicting protein function.

At the genomic level (Section 5), genetic elements are incorporated into
networks by extracting coding genes’ co-expression information from
transcriptomic data. Single-cell and spatial molecular profiling have further
enabled the mapping of genetic interactions at the cellular and tissue level.
Investigating the cellular circuitry of molecular functions in the resulting
gene co-expression or regulatory networks can uncover disease mechanisms. For
instance, as implicated by the network parsimony principle [13], shortest path
length in a molecular network between disease-associated components often
correlates with causal molecular pathways [133, 4]. Learned embeddings—using
graph representation learning methods—that capture genome-wide interactions
has enhanced disease predictions, even at the resolution of tissues and single
cells.

At the therapeutics level (Section 6), networks are composed of drugs (e.g.,
small compounds), proteins, and diseases to allow the modeling of drug-drug
interactions, binding of drugs to target proteins, and identification of drug
repurposing opportunities. For example, by the corollary of the local
hypothesis [13], the topology of drug combinations are indicative of
synergistic or antagonistic relationships [207]. Learning the topology of
graphs containing drug, protein, and disease nodes has improved predictions of
candidate drugs for treating a disease, identification of potential off-target
effects, and prioritization of novel drug combinations.

Finally, at the healthcare level (Section 7), patient records, such as medical
images and EHRs, can be represented as networks and incorporated into protein,
disease, and drug networks. As an example, given by three network medicine
principles, the local hypothesis, the shared components hypothesis, and the
disease module hypothesis [13], rare disease patients with common neighbors
and topology likely have similar phenotypes and even disease mechanisms [9,
19]. Graph representation learning methods have been successful in integrating
patient records with molecular, genomic, and disease networks to personalize
patient predictions.

##  4 Graph representation learning for molecules

Graph representation learning has been widely used for predicting protein
interactions and function [59, 100]. Specifically, the inductive ability of
graph convolution neural networks to generalize to data points unseen during
model training (Section 2.2) and even generate new data points from scratch
graph by decoding latent representation from the embedding space (Section 2.3)
has enabled the discovery of new molecules, interactions, and functions [53,
77, 204].

###  4.1 Modeling protein molecular graphs

Computationally elucidating protein structure has been an ongoing challenge
for decades [46]. Since protein structures are folded into complex 3D
structures, it is natural to represent them as graphs. For example, we
construct a contact distance graph where nodes are individual residues and
edges are determined by a physical distance threshold [88]. Edges can also be
defined by the ordering of amino acids in the primary sequence [88]. Spatial
relationships between residues (e.g., distances, angles) may be used as
features for edges [61].

We then model the 3D protein structures by capturing dependencies in their
sequences of amino acids (e.g., applying GNNs (Section 2.2) to learn each
node’s local neighborhood structure) in order to generate protein embeddings
[92, 61]. After learning short- and long-range dependencies across sequences
corresponding to their 3D structures to produce embeddings of proteins, we can
predict primary sequences from 3D structures [92]. Alternatively, we can use a
hierarchical process of learning atom connectivity motifs to capture molecular
structure at varying levels of granularity (e.g., at the motif-,
connectivity-, and atomic-levels) in our protein embeddings, with which we can
generate new 3D structures—a difficult task due to computational constraints
of being both generalizable across different classes of molecules and flexible
to a wide range of sizes [97]. As the field of machine learning for molecules
is vast, we refer readers to existing reviews on molecular design [56, 46],
graph generation [71], and molecular properties prediction [46, 191], and
Section 6.1 on therapeutic compound design and generation.

###  4.2 Quantifying protein interactions

Many have integrated various data modalities, including chemical structure,
binding affinities, physical and chemical principles, and amino acid
sequences, to improve protein interaction quantification [46]. GNNs (as
described in Section 4.1) are commonly used to generate representations of
proteins based on chemical (e.g., free electrons’ and protons donors’
locations) and geometric (e.g., distance-dependent curvature) features to
predict protein pocket-ligand and protein-protein interactions sites [62];
intra- and inter-molecule residue contact graphs to predict intra- and inter-
molecular energies, binding affinities, and quality measures for a pair of
molecular complexes [25]; and ligand- and receptor-protein graphs to predict
whether a pair of residues from the ligand and receptor proteins is a part of
an interface [61]. Combining evolutionary, topological, and energetic
information about molecules enables the scoring of docked conformations based
on the similarity of random walks simulated on a pair of protein graphs (refer
to graph kernel metrics in SI Note 3) [64].

Due to experimental and resource constraints, the most updated PPI networks
are still limited in their number of nodes (proteins) and edges (physical
interactions) [122]. Topology-based methods have been shown to capture and
leverage the dynamics of biological systems to enrich existing PPI networks
[113]. Predominant methods first apply graph convolutions (Section 2.2) to
aggregate structural information in the graphs of interest (e.g., PPI
networks, ligand-receptor networks), then use sequence modeling to learn the
dependencies in amino acid sequences, and finally concatenate the two outputs
for predicting the presence of physical interactions [121, 204].
Interestingly, such concatenated outputs have been treated as “image" inputs
to CNNs [121], demonstrating the synergy of graph- and non-graph based machine
learning methods. Similar graph convolution methods are also used to remove
less credible interactions, thereby constructing a more reliable PPI network
[206].

###  4.3 Interpreting protein functions and cellular phenotypes

Characterizing a protein’s function in specific biological contexts is a
challenging and experimentally intensive task [136, 228]. However, innovating
graph representation learning techniques to represent protein structures and
interactions has facilitated protein function prediction [224], especially
when we leverage existing gene ontologies and transcriptomic data.

Gene Ontology (GO) terms [42] are a standardized vocabulary for describing
molecular functions, biological processes, and cellular locations of gene
products [223]. They have been built as a hierarchical graph that GNNs then
leverage to learn dependencies of the terms [223], or directly used as protein
function labels [58, 59]. In the latter case, we typically construct sequence
similarity networks, combine them with PPI networks, and integrate protein
features (e.g. amino acid sequence, protein domains, subcellular location,
gene expression profiles) to predict protein function [58, 59]. Others have
even created gene interaction networks using transcriptomic data [53, 79] to
capture context-specific interactions between genes, which PPI networks lack.

Alternative graph representation learning methods for predicting protein
function include defining diffusion-based distance metrics on PPI networks for
predicting protein function [24]; using the theory of topological persistence
to compute signatures of a protein based on its 3D structure [49]; and
applying topological data analysis to extract features from protein contact
networks created from 3D coordinates [130] (SI Note 3). Many have also adopted
an attention mechanism for protein sequence embeddings generated by a
Bidirectional Encoder Representations from Transformers (BERT) model to enable
interpretability [139, 182], showcasing the synergy of graph-based and
language models.

Learning multi-scale representations of proteins and cell types (Figure 4a)

Graph dataset. Activation of gene products can vary considerably across cells.
Single-cell transcriptomic and proteomic data captures the heterogeneity of
gene expression across diverse types of cells [167, 105]. With the help of
GNNs, we inject cell type specific expression information into our
construction of cell type specific gene interaction networks [155, 135, 115].
To do so, we need a global protein interaction network [166, 122].

Learning task. On a global gene interaction network, we perform multilabel
node classification to predict whether a gene is activated in a specific cell
type based on single-cell RNA sequencing (scRNA-seq) experiments. In
particular, if there are $N$ cell types identified in a given experiment, each
gene is associated with a vector of length $N$. Given the gene interaction
network and label vectors for a select number of genes, the task is to train a
model that predicts every element of the vector for a new gene such that
predicted values indicate the probabilities of gene activation in various cell
types (Figure 4a). To enable inductive learning, we split our nodes (i.e.,
genes) into train, validation, and test sets such that we can generalize to
never-before-seen genes.

Impact. Generating gene embeddings that consider differential expression at
the cell type level enables predictions at a single cell resolution, with
considerations for factors including disease/cell states and temporal/spatial
dependencies [155, 153]. Implications of such cell-type aware gene embeddings
extend to cellular function prediction and identification of cell-type-
specific disease features [115]. For example, quantifying ligand-receptor
interactions using single cell expression data has elucidated intercellular
interactions in tumor microenvironments (e.g., via CellPhoneDB [55] or
NicheNet [18]). In fact, upon experimental validation of the predicted cell-
cell interactions in distinct spatial regions of tissues and/or tumors, these
studies have demonstrated the importance of spatial heterogeneity in tumors
[219]. Further, unlike most non-graph based methods, like autoencoders, GNNs
are able to model dependencies (e.g., physical interactions) between proteins
as well as single-cell expression [8, 176].

##  5 Graph representation learning for genomics

Diseases are classified based on the presenting symptoms of patients, which
can be caused by molecular dysfunctions, such as genetic mutations. As a
result, diagnosing diseases requires knowledge about alterations in the
transcription of coding genes to capture genome-wide associations driving
disease acquisition and progression. Graph representation learning methods
allow us to analyze heterogeneous networks of multimodal data and make
predictions across domains, from genomic level data (e.g., gene expression,
copy number information) to clinically relevant data (e.g., pathophysiology,
tissue localization).

###  5.1 Leveraging gene expression measurements

Comparing transcriptomic profiles from healthy individuals to those of
patients with a specific disease informs clinicians of its causal genes. As
gene expression is the direct readout of perturbation effects, changes in gene
expression are often used to model disease-specific co-expression or
regulatory interactions between genes. Further, injecting gene expression data
into PPI networks has identified disease biomarkers, which are then used to
more accurately classify diseases of interest.

Methods that rely solely on gene expression data typically transform the co-
expression matrix into a more topologically meaningful form [78, 127, 143].
Gene expression data can be transformed into a colored graph that captures the
shape of the data (e.g., using TDA [143]; refer to SI Note 3), which then
enables downstream analysis using network science metrics and graph machine
learning. Topological landscapes present in gene expression data can be
vectorized and fed into a GCN to classify the disease type [127].
Alternatively, gene expression data can directly be used to construct disease
and gene networks that are then input into a joint matrix factorization and
GCN method to draw disease-gene associations, akin to a recommendation task
(Section 2.2) [78]. Further, applying a joint GCN, VAE, and GAN framework
(Section 2.3) to gene correlation networks—initialized with a subset of gene
expression matrices—can generate disease networks with the desired properties
[203].

Because gene expression data can be noisy and variational, recent advances
include fusing the co-expression matrices with existing biomedical networks,
such as GO annotations and PPI, and feeding the resulting graph into graph
convolution layers (Section 2.2) [36, 45, 154]. Doing so has enabled more
interpretable disease classification models (e.g., weighting gene interactions
based on existing biological knowledge). However, despite the utility of PPI
networks, they have been reported to limit models trained solely on PPI
networks because they are unable to capture all gene regulatory activities
[152]. To this end, graph representation learning methods, such as GNNs, have
been developed to learn robust and meaningful representations of molecules
despite the incomplete interactome [90] and to inductively infer new edges
between pairs of nodes [119].

###  5.2 Injecting single cell and spatial information into molecular networks

Single-cell RNA sequencing (scRNA-seq) data lend themselves to graph
representation learning to model cellular differential processes [20, 155] and
disease states [153]. In particular, a predominant approach to analyze scRNA-
seq datasets is to transform them into gene similarity networks, such as gene
co-expression networks, or cell similarity networks by correlating gene
expression readouts across individual cells. Applied to such networks, graph
representation learning can impute scRNA-seq data [89, 186], predict cell
clusters [28, 186], etc. Cell similarity graphs have also been created using
autoencoders by first embedding gene expression readouts and then connecting
genes based on how similar their embeddings are [186]. Alternatively,
variational graph autoencoders produce cell embeddings and interpretable
attention weights indicating what genes the model attends to when deriving an
embedding for a given cell [21]. Beyond GNNs and graph autoencoders, learning
a manifold over a cell state space can quantify the effects of experimental
perturbations [20]. To this end, cell similarity graphs are constructed for
control and treated samples and used to estimate the likelihood of a cell
population observed under a given perturbation [20].

Spatial molecular profiling can measure both gene expression at the cellular
level and location of cells in a tissue [131]. As a result, spatial
transcriptomics data can be used to construct cell graphs [213], spatial gene
expression graphs [146], gene co-expression networks, or molecular similarity
graphs [80]. Creating cell neighborhood and spatial gene expression graphs
require a distance metric, as edges are determined based on spatial proximity,
while gene co-expression and molecular similarity graphs need a threshold
applied on the gene expression data [80]. From such networks, graph
representation learning methods produce embeddings that capture the network
topology and that can be further optimized for downstream tasks. For instance,
a cell neighborhood graph and a gene pair expression matrix enable classic
GNNs to predict ligand-receptor interactions [213]. In fact, as ligand-
receptor interactions are directed, they could be used to infer causal
interactions of previously unknown ligand-receptor pairs [132, 213].

Learning representations of diseases and phenotypes (Figure 4b)

Graph dataset. Symptoms are observable characteristics that typically result
from interactions between genotypes. Physicians utilize a standardized
vocabulary of symptoms, i.e., phenotypes, to describe human diseases. Thus, we
model diseases as collections of associated phenotypes to diagnose patients
based on their presenting symptoms. Consider a graph built from the
standardized vocabulary of phenotypes, e.g., the Human Phenotype Ontology
[156] (HPO). The HPO forms a directed acyclic graph with nodes representing
phenotypes and edges indicating hierarchical relationships between them;
however, it is typically treated as an undirected graph in most
implementations of GNNs. A disease described by a set of its phenotypes
corresponds to a subset of nodes in the HPO, forming a subgraph of the HPO.
Note that a subgraph can contain many disconnect components dispersed across
the entire graph [9].

Learning task. Given a dataset of HPO subgraphs and disease labels for a
select number of them, the task is to generate an embedding for every subgraph
and use the learned subgraph embeddings to predict the disease most consistent
with the set of phenotypes that the embedding represents [9] (Figure 4b).

Impact. Modeling diseases as rich graph structures, such as subgraphs, enables
a more flexible representation of diseases than relying on individual nodes or
edges. As a result, we can better resolve complex phenotypic relationships and
improve differentiation of diseases or disorders.

##  6 Graph representation learning for therapeutics

Modern drug discovery requires elucidating a candidate drug’s chemical
structure, identifying its drug targets, quantifying its efficacy and
toxicity, and detecting its potential side effects [13, 69, 85, 151]. Because
such processes are costly and time-consuming, in silico approaches have been
adopted into the drug discovery pipeline. However, cross-domain expertise is
necessary to develop a drug with the optimal binding affinity and specificity
to biomarkers, maximal therapy efficacy, and minimal adverse effects. As a
result, it is critical to integrate chemical structure information, protein
interactions, and clinically relevant data (e.g., indications and reported
side effects) into predictive models for drug discovery and repurposing. Graph
representation learning has been successful in characterizing drugs at the
systems level without patient data to make predictions about interactions with
other drugs, protein targets, side effects, diseases [33, 35, 63, 74, 126,
217].

###  6.1 Modeling compound molecular graphs

Similar to proteins, small compounds are modeled as 2D and 3D molecular graphs
such that nodes are atoms and edges are bonds. Each atom and bond may include
features, such as atomic mass, atomic number, and bond type, to be included in
the model [168, 162]. Edges can also be added to indicate pairwise spatial
distance between two atoms [65], or directed with information on bond angles
and rotations incorporated into the molecular graph [104].

Representing molecules as graphs has improved predictions on various quantum
chemistry properties. Intuitively, message passing steps (i.e., in GNNs
(Section 2.2)) aggregate information from neighboring atoms and bonds to learn
the local chemistry of each atom [168]. For example, generating
representations of the atoms, distances, and angles to be propagated along the
molecular graph has allowed us to identify the angle and direction of
interactions between atoms [104]. Producing atom-centered representations
based on a weighted combination of their neighbors’ features (i.e., using an
attention mechanism) is able to model interactions among reactants for
predicting organic reaction outcomes [41]. Alternatively, molecular graphs
have been decomposed into a “junction tree," where each node represents a
substructure in the molecule, to learn representations of both the molecular
graph and the junction tree for generating new molecules with desirable
properties (Section 2.3) [98]. Due to the major challenge of finding novel and
diverse compounds with specific chemical properties, iteratively editing
fragments of a molecular graph during training has improved predictions for
high-quality candidates targeting our proteins of interest [197].

###  6.2 Quantifying drug-drug and drug-target interactions

Corresponding to molecular structure is binding affinity and specificity to
biomarkers. Such measurements are important for ensuring that a drug is
effective in treating its intended disease, and does not have significant off-
target effects [191]. However, quantifying these metrics requires labor- and
cost-intensive experiments [46, 191]. Modeling small compounds’ and protein
targets’ molecular structure as well as their binding affinities and
specificity, for instance, using graph representation learning has enabled
accelerated investigation of interactions between a given drug and protein
target.

First, we learn representations of drugs and targets using graph-based
methods, such as TDA [6] or shallow network embedding approaches [68].
Concretely, TDA (refer to SI Note 3) transforms experimental data into a graph
where nodes represent compounds and edges indicate a level of similarity
between them [6]. Shallow network embedding techniques are also used to
generate embeddings for drugs and targets by computing drug-drug, drug-target,
and target-target similarities (Section 2.1) [175]. Non-graph based methods
have also been used to construct graphs that are then fed into a graph
representation learning model to generate embeddings. K-nearest neighbors, for
instance, is a common used method to construct drug and target similarity
networks [174]. The resulting embeddings are fed into downstream machine
learning models.

Fusing compound sequence, structure, and clinical implications has
significantly improved drug-drug and drug-target interaction predictions. For
example, attention mechanisms have been applied on drug graphs, with chemical
structures and side effects as features, to generate interpretable predictions
of drug-drug interactions [124]. Also, two separate GNNs may be used to learn
representations of protein and small molecule graphs for predicting drug-
target affinity [94]. To be flexible with other graph- and non-graph-based
methods, protein structure representations generated by graph convolutions has
been combined with protein sequence representations (e.g., shallow network
embedding methods or CNNs) to predict the probability of compound-protein
interactions [30, 150, 178, 117].

###  6.3 Identifying drug-disease associations and biomarkers for complex
disease

Part of the drug discovery pipeline is minimizing adverse drug events [46,
191]. But, in addition to high financial cost, the experiments required to
measure drug-drug interactions and toxicity face a combinatorial explosion
problem [46]. Graph representation learning methods enables in silico modeling
of drug action, which allows for more efficient ranking of candidate drugs for
repurposing, such as by considering gene expression data, gene ontologies,
drug similarity, and other clinically relevant data regarding side effects and
indications.

Drug and disease representations have been learned on homogeneous graphs of
drugs, diseases, or targets. For instance, Medical Subject Headings (MeSH)
terms may be used to construct a drug-disease graph, from which latent
representations of drugs and diseases are learned using various graph
embedding algorithms, including DeepWalk and LINE (Section 2.1) [72]. TDA
(refer to SI Note 3) has also been applied to construct graphs of drugs,
targets, and diseases separately, from which representations of such entities
are learned and optimized for downstream prediction [187].

To emphasize the systems-level complexity of diseases, recent methods fuse
multimodal data to generate heterogeneous graphs. For example, neighborhood
information are aggregated from heterogeneous networks comprised of drug,
target, and disease information to predict drug-target interactions (Section
2.2) [184]. In other instances, PPI networks are combined with genomic
features to predict drug sensitivity using GNNs [196]. As a result, approaches
that integrate cross-domain knowledge as a vast heterogeneous network and/or
into the model’s architecture seem better equipped to elucidate drug action.

Learning representations of drugs and drug combinations (Figure 4c)

Graph dataset. Combination therapies are increasingly used to treat complex
and chronic diseases. However, it is experimentally intensive and costly to
evaluate whether two or more drugs interact with each other and lead to
effects that go beyond additive effects of individual drugs in the
combination. Graph representation learning has been equipped to leverage
perturbation experiments performed across cell lines to predict the response
of never-before-seen cell lines with mutation(s) of interest (e.g., disease-
causing) to drug combinations. Consider a multimodal network of protein-
protein, protein-drug, and drug-drug interactions where nodes are proteins and
drugs, and edges of different types indicate physical contacts between
proteins, the binding of drugs to their target proteins, and interactions
between drugs (e.g., synergistic effects, where the effects of the combination
are larger than the sum of each drug’s individual effect) [226, 95]. Such a
multimodal drug-protein network is constructed for every cell line, yielding a
collection of cell line specific networks (Figure 4c) [95].

Learning task. From a single cell line’s drug-protein network, we predict
whether two or more drugs are interacting in the cell line [95]. Concretely,
we embed nodes of a drug-protein network into a compact embedding space such
that distances between node embeddings correspond to similarities of nodes’
local neighborhoods in the network. We then use the learned embeddings to
decode drug-drug edges, and predict probabilities of two drugs interacting
based on their embeddings. Next, we apply transfer learning to leverage the
knowledge gained from one cell line specific network to accelerate the
training and improve the accuracy across other cell line specific networks
(Figure 4c) [101]. Specifically, we develop a model using one cell line’s
drug-protein network, “reuse” the model on the next cell line’s drug-protein
network, and repeat until we have trained on drug-protein networks from all
cell lines.

Impact. Not only are non-graph based methods unsuited to capture topological
dependencies between drugs and targets, most predictive models for drug
combinations do not consider tissue or cell-line specificity of drugs. Because
drugs’ effects on the body are not uniform, it is crucial to account for such
anatomical differences. Further, the ability to prioritize candidate drug
combinations in silico could reduce the cost of developing and testing them
experimentally, thereby enabling robust evaluation of the most promising
combinatorial therapies.

##  7 Graph representation learning for healthcare

Graph representation learning has been used to fuse multimodal knowledge with
patient records to better enable precision medicine. Two modes of patient data
successfully integrated using deep graph learning are histopathological images
[15, 31] and EHRs [38, 116].

###  7.1 Leveraging networks for diagnostic imaging

Medical images of patients, including histopathology slides, enable clinicians
to comprehensively observe the effects of a disease on the patient’s body
[73]. Medical images, such as large whole histopathology slides, are typically
converted into cell spatial graphs, where nodes represent cells in the image
and edges indicate that a pair of cells is adjacent in space. Deep graph
learning has been shown to detect subtle signs of disease progression in the
images while integrating other modalities (e.g., tissue localization [147] and
genomic features [31]) to improve medical image processing.

Cell-tissue graphs generated from histopathological images are able to encode
the spatial context of cells and tissues for a given patient. Cell morphology
and tissue micro-architecture information can be aggregated from cell graphs
to grade cancer histology images (e.g., using GNNs (Section 2.2)) [2, 11, 31,
225]. An example aggregation method is pooling with an attention mechanism to
infer relevant patches in the image [2]. Further, cell morphology and
interactions, tissue morphology and spatial distribution, cell-to-tissue
hierarchies, and spatial distribution of cells with respect to tissues can be
captured in a cell-to-tissue graph, upon which a hierarchical GNN can learn
representations using these different data modalities [147]. Because
interpretability is critical for models aimed to generate patient-specific
predictions, post-hoc graph pruning optimization may be performed on a cell
graph generated from a histopathology image to define subgraphs explaining the
original cell graph analysis [93].

Graph representation learning methods have also been proven successful for
classifying other types of medical images. GNNs are able to model
relationships between lymph nodes to compute the spread of lymph node gross
tumor volume based on radiotherapy CT images (Section 2.2) [26]. MRI images
can be converted into graphs that GNNs are applied to for classifying the
progression of Alzheimer’s Disease [10, 165, 190]. GNNs are also shown to
leverage relational structures like similarities among chest X-rays to improve
downstream tasks, such as disease diagnosis and localization [128].
Alternatively, TDA can generate graphs of whole-slide images, which include
tissues from various patient sources (SI Note 3), and GNNs are then used to
classify the stage of colon cancer [111].

Further, spatial molecular profiling benefits from methodological advancements
made for medical images. With spatial gene expression graphs (weighted and
undirected) and corresponding histopathology images, gene expression
information are aggregated to generate embeddings of genes that could then be
used to investigate spatial domains (e.g., differentiate between cancer and
noncancer regions in tissues) [84]. Since multimodal data enables more robust
predictions, GNNs (Section 2.2) have been applied to generate cell spatial
graphs from histopathology images and then fuse genomic and transcriptomic
data for predicting treatment response and resistance, histopathology grading,
and patient survival [31].

###  7.2 Personalizing medical knowledge networks with patient records

Electronic health records are typically represented by ICD (International
Classification of Disease) codes [38, 116]. The hierarchical information
inherent to ICD codes (medical ontologies) naturally lend itself to creating a
rich network of medical knowledge. In addition to ICD codes, medical knowledge
can take the form of other data types, including presenting symptoms,
molecular data, drug interactions, and side effects. By integrating patient
records into our networks, graph representation learning is well-equipped to
advance precision medicine by generating predictions tailored to individual
patients.

Methods that embed medical entities, including EHRs and medical ontologies,
leverage the inherently hierarchical structure in the medical concepts KG
[159]. Low dimensional embeddings of EHR data can be generated by separately
considering medical services, doctors, and patients in shallow network
embeddings (Section 2.1) and graph neural networks (Section 2.2) [194, 129].
Alternatively, attention mechanisms may be applied on EHR data and medical
ontologies to capture the parent-child relationships [38, 123, 172]. Rather
than assuming a certain structure in the EHRs, a Graph Convolution Transformer
can even learn the hidden EHR structure [39].

EHRs also have underlying spatial and/or temporal dependencies [29] that many
methods have recently taken advantage of to perform time-dependent prediction
tasks. A mixed pooling multi-view self-attention autoencoder has generated
patient representations for predicting either a patient’s risk of developing a
disease in a future visit, or the diagnostic codes of the next visit [40]. A
combined LSTM and GNN model has also been used to represent patient status
sequences and temporal medical event graphs, respectively, to predict future
prescriptions or disease codes (Section 2.2) [120, 110]. Alternatively, a
patient graph may be constructed based on the similarity of patients, and
patient embeddings learned by an LSTM-GNN architecture are optimized to
predict patient outcomes [157]. An ST-GCN [201] is designed to utilize the
underlying spatial and temporal dependencies of EHR data for generating
patient diagnoses [116].

EHRs are often supplemented with other modalities, such as diseases, symptoms,
molecular data, drug interactions, etc [29, 107, 140, 221]. A probabilistic KG
of EHR data, which include medical history, drug prescriptions, and laboratory
examination results, has been used to consider the semantic relations between
EHR entities in a shallow network embedding method (Section 2.1) [114]. Meta-
paths may alternatively be exploited in an EHR-derived KG to leverage higher
order, semantically important relations for disease classification [82].
Initializing node features for drugs and diseases using Skipgram and then
applying GNN leverages multi-layer message-passing to predict adverse drug
events [107]. Moreover, combined RNNs and GNNs models have been applied to EHR
data integrated with drug and disease interactions to better recommend
medication combinations [163].

Fusing personalized health information with knowledge graphs (Figure 4d)

Graph dataset. To realize precision medicine, we need robust methods that can
inject biomedical knowledge into patient-specific information to produce
actionable and trustworthy predictions [43]. Since EHRs can also be
represented by networks, we are able to fuse patients’ EHR networks with
biomedical networks, thus enabling graph representation learning to make
predictions on patient-specific features. Consider a knowledge graph, where
nodes and edges represent different types of bioentities and their various
relationships, respectively. Examples of relations may include “up-/down-
regulate,” “treats,” “binds,” “encodes,” and “localizes” [140]. To integrate
patients into the network, we create a distinct meta node to represent each
patient, and add edges between the patient’s meta node and its associated
bioentity nodes (Figure 4d).

Learning tasks. We learn node embeddings for each patient while predicting
(via edge regression) the probability of a patient developing a specific
disease or of a drug effectively treating the patient (Figure 4d) [140].

Impact. Precision medicine requires an understanding of patient-specific data
as well as the underlying biological mechanisms of disease and healthy states.
Most networks do not consider patient data, which can prevent robust
predictions of patients’ conditions and potential responsiveness to drugs. The
ability to integrate patient data with biomedical knowledge can address such
issues.

Figure 4: Representation learning in four areas of biology and medicine. We
present a case study on (a) cell-type aware protein representation learning
via multilabel node classification (details in Box 4.3), (b) disease
classification using subgraphs (details in Box 5.2), (c) cell-line specific
prediction of interacting drug pairs via edge regression with transfer
learning across cell lines (details in Box 6.3), and (d) integration of health
data into knowledge graphs to predict patient diagnoses or treatments via edge
regression (details in Box 7.2).

##  8 Outlook

As graph representation learning has aided in the mapping of genotypes to
phenotypes, leveraging graph representation learning for fine-scale mapping of
variants is a promising new direction [193]. By re-imagining GWAS and
expression Quantitative Trait Loci (eQTL) studies [179] as networks, we can
already begin to discover biologically meaningful modules to highlight key
genes involved in the underlying mechanisms of a disease [188]. We can
alternatively seed network propagation with QTL candidate genes [193].
Additionally, because graphs can model long-range dependencies or
interactions, we can model chromatin elements and the effects of their binding
to regions across the genome as a network [48, 108]. We could even reconstruct
3D chromosomal structures by predicting 3D coordinates for nodes derived from
a Hi-C contact map [83]. Further, as spatial molecular profiling has enabled
profound discoveries for diseases, graph representation learning repertoire to
analyze such datasets will continue to expand. For instance, with dynamic
GNNs, we may be able to better capture changes in expression levels observed
in single cell RNA sequencing data over time or as a result of a perturbation
[20, 50].

Effective integration of healthcare data with knowledge about molecular,
genomic, disease-level, and drug-level data can help generate more accurate
and interpretable predictions about the biological systems of health and
disease [60]. Given the utility of graphs in both the biological and medical
domains, there has been a major push to generate knowledge graphs that
synthesize and model multi-scaled, multi-modal data, from genotype-phenotype
associations to population-scale epidemiological dynamics. In public health,
spatial and temporal networks can model space- and time-dependent observations
(e.g., disease states, susceptibility to infection [125]) to spot trends,
detect anomalies, and interpret temporal dynamics.

As artificial intelligence tools implementing graph representation learning
algorithms are increasingly employed in clinical applications, it is essential
to ensure that representations are explainable [208], fair [3], and robust
[218], and that existing tools are revisited in light of algorithmic bias and
health disparities [144].

## Acknowledgements

We gratefully acknowledge the support of NSF under Nos. IIS-2030459 and
IIS-2033384, US Air Force Contract No. FA8702-15-D-0001, Harvard Data Science
Initiative, Amazon Research Award, Bayer Early Excellence in Science Award,
AstraZeneca Research, and Roche Alliance with Distinguished Scientists Award.
M.M.L. is supported by T32HG002295 from the National Human Genome Research
Institute and a National Science Foundation Graduate Research Fellowship. Any
opinions, findings, conclusions or recommendations expressed in this material
are those of the authors and do not necessarily reflect the views of the
funders. The authors declare that there are no conflicts of interests.

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