The DOI matches its Crossref record; 200 of the 218 references it cites were found in a registry or an index; 3 of the 169 sources its claims cite were checked for support.
Graph representation learning in biomedicine and healthcare
Open accessAbstract
Networks—or graphs—are universal descriptors of systems of interacting elements. In biomedicine and healthcare, they can represent, for example, molecular interactions, signalling pathways, disease co-morbidities or healthcare systems. In this Perspective, we posit that representation learning can realize principles of network medicine, discuss successes and current limitations of the use of representation learning on graphs in biomedicine and healthcare, and outline algorithmic strategies that leverage the topology of graphs to embed them into compact vectorial spaces. We argue that graph representation learning will keep pushing forward machine learning for biomedicine and healthcare applications, including the identification of genetic variants underlying complex traits, the disentanglement of single-cell behaviours and their effects on health, the assistance of patients in diagnosis and treatment, and the development of safe and effective medicines. This Perspective outlines the successes and limitations of graph deep learning for biomedical and healthcare applications.
Overview
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Written from the paper’s full text.
TL;DR
This review argues that long-standing systems biology and medicine principles—such as the local hypothesis, shared components hypothesis, disease module hypothesis, and network parsimony—provide conceptual grounding for graph representation learning in biomedicine. It surveys shallow embeddings, graph neural networks, and generative graph models across molecular, genomic, therapeutic, and healthcare applications.
Key findings
- Graph representation learning learns compact vector embeddings in which nodes with similar network neighborhoods are placed close together, and it can be viewed as a differentiable engine of network biomedicine principles including the local hypothesis, shared components hypothesis, disease module hypothesis, and network parsimony.
- The reviewed methods span shallow graph embeddings (e.g., DeepWalk, node2vec, LINE, knowledge graph embeddings), graph neural networks with message passing, neighborhood aggregation, and representation updates, and deep generative graph models using variational autoencoders, generative adversarial networks, junction trees, and sequential node/edge sampling.
- Biomedical applications covered include protein structure, protein interactions, and protein function; gene expression, single-cell RNA sequencing, and spatial molecular profiling; drug-target, drug-drug, and drug-disease prediction; and patient-specific networks from histopathology images and electronic health records.
- No primary numerical results or benchmark performance numbers are reported in this review.
Methods
- Design: narrative review/survey, not a primary empirical study; it synthesizes graph representation learning approaches and biomedical applications.
- Data or sample: no new dataset is analyzed; the paper discusses published methods and application areas across molecular, genomic, therapeutic, and healthcare domains.
- Main analysis: conceptual synthesis linking graph representation learning to systems biology and medicine principles, organized into shallow graph embedding approaches, graph neural networks, and generative graph models.
Limitations
- Stated in the paper: graphs contain complex topographical structure, have no fixed node ordering or reference points, and comprise many different entity and edge types, which classic deep learning methods are unable to handle directly.
- Stated in the paper: biomedical networks are often incomplete or limited—for example, PPI networks are limited in nodes and edges due to experimental and resource constraints, gene expression data can be noisy and variational, and PPI networks cannot capture all gene regulatory activities.
- Evident from the text: as a review, it provides no empirical benchmark, systematic search protocol, or quantitative comparison; it also notes that explainability, fairness, and robustness of graph representations remain essential but unresolved needs in clinical applications.
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Claims the paper makes
96 Not checked yet · 23 No basis given · 12 Own interpretation · 1 Partly supported · accuracy not yet measured
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Two modes of patient data successfully integrated using deep graph learning are histopathological images [8, 185] and EHRs [186, 187].”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for healthcare
What the sources say
- Supportedcites [8] Chen, R. J. et al. Pathomic fusion: an integrated framework for fusing histopathology and genomic features for cancer diagnosis and prognosis. In IEEE Transactions on Medical Imaging Vol. 41, 757–770 (IEEE, 2022). source
This source is cited for histopathological images, not EHRs, which have separate citations. In Methods, the paper describes constructing cell graphs from histopathology images and using graph convolutional networks to learn representations for outcome prediction; it also uses those representations in Pathomic Fusion. Page numbers are not available in our copy. (LLM, conf=0.90)
- Not checked yetcites [185] Levy, J., Haudenschild, C., Bar, C., Christensen, B. & Vaickus, L. Topological feature extraction and visualization of whole slide images using graph neural networks. Pac. Symp. Biocomput. 26, 285–296 (2021).
- Supportedcites [186] Hu, J. et al. SpaGCN: integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network. Nat. Methods 18, 1342–1351 (2021). source
The citation to this paper is attached to histopathological images, not EHRs. In “Overview of SpaGCN and evaluation,” the paper says SpaGCN uses histology information to build a graph and a graph convolutional layer to analyze gene expression. “Application to human primary pancreatic cancer tissue” reports that its detected domain corresponded better to the cancer region annotated on the histology image. (LLM, conf=0.91)
- Partly supportedcites [187] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S. & Sontag, D. Learning a health knowledge graph from electronic medical records. Sci. Rep. 7, 5994 (2017). sourceExperimental — accuracy not yet measured
The Contributions section reports learning a disease–symptom knowledge graph from electronic medical records, supporting the EHR portion of the cited sentence. But Methods > Algorithms for constructing a knowledge graph specifies logistic regression, naive Bayes, and a noisy-OR Bayesian network—not deep graph learning. The histopathological-image portion is cited to other sources and is not assessed here. (LLM, conf=0.89)
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In the paper
“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.”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning > Graph neural networks
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In the paper
“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 [16].”
§ Graph Representation Learning in Biomedicine and Healthcare > Introduction
What the sources say
- Not checked yetcites [13] Barabási, A.-L., Gulbahce, N. & Loscalzo, J. Network medicine: a network-based approach to human disease. Nat. Rev. Genet. 12, 56–68 (2011). source
- Not checked yetcites [16] Agrawal, M., Zitnik, M. & Leskovec, J. Large-scale analysis of disease pathways in the human interactome. In Pac. Symp. Biocomput. 23, 111–122 (2018).
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“Topology-based methods have been shown to capture and leverage the dynamics of biological systems to enrich existing PPI networks [119].”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for molecules > Quantifying protein interactions
What the sources say
- Not checked yetcites [119] Moreau, Y. & Tranchevent, L.-C. Computational tools for prioritizing candidate genes: boosting disease gene discovery. Nat. Rev. Genet. 13, 523–536 (2012). source
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“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 by decoding latent representation from the embedding space (Section 2.3) has enabled the discovery of new molecules, interactions, and functions [101, 108, 109].”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for molecules
What the sources say
- Not checked yetcites [101] Huang, K., Xiao, C., Glass, L. M., Zitnik, M. & Sun, J. SkipGNN: predicting molecular interactions with skip-graph networks. Sci. Rep. 10, 21092 (2020). source
- Not checked yetcites [108] Fout, A., Byrd, J., Shariat, B. & Ben-Hur, A. Protein interface prediction using graph convolutional networks. In Proc. International Conference on Neural Information Processing Systems (eds Guyon, I. et al.) (Curran Associates, 2019).
- Not checked yetcites [109] Ingraham, J., Garg, V. K., Barzilay, R. & Jaakkola, T. S. Generative models for graph-based protein design. In Proc. International Conference on Neural Information Processing Systems (eds Wallach, H. et al.) (Curran Associates, 2019).
plaindirect- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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)”
§ Graph Representation Learning in Biomedicine and Healthcare > Introduction
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In the paper
“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)”
§ Graph Representation Learning in Biomedicine and Healthcare > Introduction
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In the paper
“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.”
§ Graph Representation Learning in Biomedicine and Healthcare > Application areas in biology and medicine
What the sources say
- Not checked yetcites [16] Agrawal, M., Zitnik, M. & Leskovec, J. Large-scale analysis of disease pathways in the human interactome. In Pac. Symp. Biocomput. 23, 111–122 (2018).
- Not checked yetcites [45] Menche, J. et al. Uncovering disease-disease relationships through the incomplete interactome. Science 347, 1257601 (2015). source
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“In protein interaction networks, for instance, mutations in interacting proteins often lead to similar diseases [13].”
§ Graph Representation Learning in Biomedicine and Healthcare > Introduction
What the sources say
- Not checked yetcites [13] Barabási, A.-L., Gulbahce, N. & Loscalzo, J. Network medicine: a network-based approach to human disease. Nat. Rev. Genet. 12, 56–68 (2011). source
plaindirect- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We posit that representation learning can realize network biomedicine principles.”
§ Graph Representation Learning in Biomedicine and Healthcare > Introduction
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- Probably not a substantial claim. 42
- It may state two separate claims as one. 27
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In the paper
“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 in Biomedicine and Healthcare > Graph representation learning for genomics
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
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In the paper
“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.”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“We also capture the breadth of ways in which representation learning has dramatically improved the state-of-the-art in biomedical machine learning.”
§ Graph Representation Learning in Biomedicine and Healthcare > Abstract
boosteddirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“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.”
§ Graph Representation Learning in Biomedicine and Healthcare > Abstract
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“These approaches are transductive embedding methods where the encoder function is a simple embedding lookup (Figure 2).”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning > Shallow graph embedding approaches
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
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In the paper
“Graph neural networks (GNNs) are a class of neural networks designed for graph-structured datasets (Figure 2).”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning > Graph neural networks
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“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].”
§ Graph Representation Learning in Biomedicine and Healthcare > Introduction
What the sources say
- Not checked yetcites [13] Barabási, A.-L., Gulbahce, N. & Loscalzo, J. Network medicine: a network-based approach to human disease. Nat. Rev. Genet. 12, 56–68 (2011). source
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“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.”
§ Graph Representation Learning in Biomedicine and Healthcare > Introduction
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Graph Representation Learning in Biomedicine and Healthcare > Abstract
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Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
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In the paper
“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).”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for genomics
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In the paper
“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.”
§ Graph Representation Learning in Biomedicine and Healthcare > Introduction
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In the paper
“Comparing transcriptomic profiles from healthy individuals to those of patients with a specific disease informs clinicians of its causal genes.”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for genomics > Leveraging gene expression measurements
plaindirect- Asserted: passes — The authors put it forward as true.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“We synthesize a spectrum of algorithmic approaches that, at their core, leverage graph topology to embed networks into compact vector spaces.”
§ Graph Representation Learning in Biomedicine and Healthcare > Abstract
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None of the sources cited for this claim have been checked yet.
In the paper
“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 [116]; intra- and inter-molecule residue contact graphs to predict intra- and inter-molecular energies,”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for molecules > Quantifying protein interactions
What the sources say
- Not checked yetcites [116] Li, D. & Gao, J. Towards perturbation prediction of biological networks using deep learning. Sci. Rep. 9, 11941 (2019). source
- Not checked yetcites [117] Liu, Y., Yuan, H., Cai, L., & Ji, S. Deep learning of high-order interactions for protein interface prediction. In Proc. ACM SIGKDD Conference on Knowledge Discovery and Data Mining 679–687 (ACM, 2020). source
- Not checked yetcites [111] Elton, D. C., Boukouvalas, Z., Fuge, M. D. & Chung, P. W. Deep learning for molecular design—a review of the state of the art. Mol. Syst. Des. Eng. 4, 828–849 (2019). source
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Further, injecting gene expression data into PPI networks has identified disease biomarkers, which are then used to more accurately classify diseases of interest.”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for genomics > Leveraging gene expression measurements
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“They learn compact representations of graph elements, their attributes, and supervised labels if any.”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning > Graph neural networks
plaindirect- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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 in Biomedicine and Healthcare > Graph representation learning
hedgeddirect- Asserted: passes — The authors put it forward as true.
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In the paper
“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.”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning > Shallow graph embedding approaches
plaindirect- Asserted: passes — The authors put it forward as true.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Deep generative models address the challenge by estimating distributional graph properties based on a dataset of graphs 𝒢 and inferring graph structures using such optimized distributions.”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning > Generative graph models
plaindirect- Asserted: passes — The authors put it forward as true.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Additionally, GNNs can optimize supervised signals and graph structure simultaneously, whereas shallow embedding approaches require a two-stage approach to achieve the same.”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning > Graph neural networks
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Since protein structures are folded into complex 3D structures, it is natural to represent them as graphs.”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for molecules > Modeling protein molecular graphs
plaindirect- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“Gene Ontology (GO) terms [125] are a standardized vocabulary for describing molecular functions, biological processes, and cellular locations of gene products [126].”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for molecules > Interpreting protein functions and cellular phenotypes
What the sources say
- Not checked yetcites [125] Hasibi, R. & Michoel, T. A Graph feature auto-encoder for the prediction of unobserved node features on biological networks. BMC Bioinform 22, 525 (2021). source
- Not checked yetcites [126] Cao, M. et al. Going the distance for protein function prediction: a new distance metric for protein interaction networks. PLoS ONE 8, e76339 (2013). source
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“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.”
§ Graph Representation Learning in Biomedicine and Healthcare > Application areas in biology and medicine
What the sources say
- Not checked yetcites [103] Fan, K. & Zhang, Y. Pseudo2GO: A graph-based deep learning method for pseudogene function prediction by borrowing information from coding genes. Front. Genet. 11, 807 (2020). source
plaindirect- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Hence, artificial intelligence methods that produce representations can be thought of as differentiable engines of key network biomedicine principles.”
§ Graph Representation Learning in Biomedicine and Healthcare > Introduction
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Diseases are classified based on the presenting symptoms of patients, which can be caused by molecular dysfunctions, such as genetic mutations.”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for genomics
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“More concretely, representation learning specifies deep, non-linear transformation functions that map nodes to points in a compact vector space, termed embeddings.”
§ Graph Representation Learning in Biomedicine and Healthcare > Introduction
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As biomedical networks can be multimodal and massive, special consideration is needed to scale GNNs to large and heterogeneous networks.”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning > Graph neural networks
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
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In the paper
“They have been built as a hierarchical graph that GNNs then leverage to learn dependencies of the terms [126], or directly used as protein function labels [106, 127].”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for molecules > Interpreting protein functions and cellular phenotypes
What the sources say
- Not checked yetcites [126] Cao, M. et al. Going the distance for protein function prediction: a new distance metric for protein interaction networks. PLoS ONE 8, e76339 (2013). source
- Not checked yetcites [106] Hamilton, W. L., Ying, Z. & Leskovec, J. Inductive representation learning on large graphs. In Proc. International Conference on Neural Information Processing Systems (eds Guyon, I. et al.) (Curran Associates, 2017).
- Not checked yetcites [127] Dey, T. K. & Mandal, S. Protein classification with improved topological data analysis. In 18th International Workshop on Algorithms in Bioinformatics (WABI) (eds Parida, L. & Ukkonen, E.) 6:1–6:13 (Schloss Dagstuhl–Leibniz-Zentrum fuer Informatik, 2018).
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
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In the paper
“Similar to proteins, small compounds are modeled as 2D and 3D molecular graphs such that nodes are atoms and edges are bonds.”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for therapeutics > Modeling compound molecular graphs
What the sources say
- Not checked yetcites [83] Schlichtkrull, M. et al. Modeling relational data with graph convolutional networks. In European Semantic Web Conference (eds Gangemi, A. et al.) 593–607 (Springer, 2018). source
- Not checked yetcites [166] Wang, R., Li, S., Cheng, L., Wong, M. H. & Leung, K. S. Predicting associations among drugs, targets and diseases by tensor decomposition for drug repositioning. BMC Bioinf. 20, 628 (2019). source
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
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In the paper
“Applied to such networks, graph representation learning can impute scRNA-seq data [156, 157], predict cell clusters [157, 158], etc.”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for genomics > Injecting single cell and spatial information into molecular networks
What the sources say
- Not checked yetcites [156] Alagappan, M., Jiang, D., Denko, N. & Koong, A. C. in Tumor Microenvironment (eds Koumenis, C. et al.) 253–268 (Springer, 2016). source
- Not checked yetcites [157] Thafar, M. A. et al. DTiGEMS+: drug–target interaction prediction using graph embedding, graph mining, and similarity-based techniques. J. Cheminformatics 12, 44 (2020). source
- Not checked yetcites [158] Thafar, M. A. et al. DTi2Vec: Drug–target interaction prediction using network embedding and ensemble learning. J. Cheminformatics 13, 71 (2021). source
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
- Rests on an assumption: A predominant approach to analyze single-cell RNA sequencing datasets is to transform them into gene similarity networks or cell similarity networks by correlating gene expression readouts across individual cells.
None of the sources cited for this claim have been checked yet.
In the paper
“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 [102] and to inductively infer new edges between pairs of nodes [154].”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for genomics > Leveraging gene expression measurements
What the sources say
- Not checked yetcites [102] Yin, N. et al. Synergistic and antagonistic drug combinations depend on network topology. PLoS ONE 9, e93960 (2014). source
- Not checked yetcites [154] Coley, C. W. et al. A graph-convolutional neural network model for the prediction of chemical reactivity. Chem. Sci. 10, 370–377 (2019). source
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“Spatial molecular profiling can measure both gene expression at the cellular level and location of cells in a tissue [160].”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for genomics > Injecting single cell and spatial information into molecular networks
What the sources say
- Not checked yetcites [160] Jiang, M. et al. Drug–target affinity prediction using graph neural network and contact maps. RSC Adv 10, 20701–20712 (2020). source
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Edges can also be added to indicate pairwise spatial distance between two atoms [72], or directed with information on bond angles and rotations incorporated into the molecular graph [84].”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for therapeutics > Modeling compound molecular graphs
What the sources say
- Not checked yetcites [72] Hu, Z., Dong, Y., Wang, K. & Sun, Y. Heterogeneous graph transformer. In Proc. ACM Web Conference 2704–2710 (ACM, 2020). source
- Not checked yetcites [84] Wang, X. et al. Heterogeneous graph attention network. In Proc. ACM Web Conference (eds Liu, L. & White, R.) 2022–2032 (ACM, 2019). source
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“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 [35].”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for genomics > Injecting single cell and spatial information into molecular networks
What the sources say
- Not checked yetcites [35] Hetzel, L., Fischer, D. S., Günnemann, S. & Theis, F. J. Graph representation learning for single cell biology. Curr. Opin. Syst. Biol. 28, 100347 (2021). source
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“As a result, spatial transcriptomics data can be used to construct cell graphs [161], spatial gene expression graphs [162], gene co-expression networks, or molecular similarity graphs [35].”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for genomics > Injecting single cell and spatial information into molecular networks
What the sources say
- Not checked yetcites [161] Chen, L. et al. TransformerCPI: improving compound–protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments. Bioinformatics 36, 4406–4414 (2020). source
- Not checked yetcites [162] Quan, Z., Guo, Y., Lin, X., Wang, Z.-J. & Zeng, X. GraphCPI: Graph neural representation learning for compound-protein interaction. In BIBM 717–722 (2019). source
- Not checked yetcites [35] Hetzel, L., Fischer, D. S., Günnemann, S. & Theis, F. J. Graph representation learning for single cell biology. Curr. Opin. Syst. Biol. 28, 100347 (2021). source
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“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, 14, 32, 164].”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for therapeutics
What the sources say
- Not checked yetcites [13] Barabási, A.-L., Gulbahce, N. & Loscalzo, J. Network medicine: a network-based approach to human disease. Nat. Rev. Genet. 12, 56–68 (2011). source
- Not checked yetcites [14] Hu, J. X., Thomas, C. E. & Brunak, S. Network biology concepts in complex disease comorbidities. Nat. Rev. Genet. 17, 615–629 (2016). source
- Not checked yetcites [32] Rai, A., Shinde, P. & Jalan, S. Network spectra for drug-target identification in complex diseases: new guns against old foes. Appl. Netw. Sci. 3, 51 (2018). source
- Not checked yetcites [164] Lin, X. et al. DeepGS: Deep representation learning of graphs and sequences for drug-target binding affinity prediction. ECAI (2020).
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Each atom and bond may include features, such as atomic mass, atomic number, and bond type, to be included in the model [83, 166].”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for therapeutics > Modeling compound molecular graphs
What the sources say
- Not checked yetcites [83] Schlichtkrull, M. et al. Modeling relational data with graph convolutional networks. In European Semantic Web Conference (eds Gangemi, A. et al.) 593–607 (Springer, 2018). source
- Not checked yetcites [166] Wang, R., Li, S., Cheng, L., Wong, M. H. & Leung, K. S. Predicting associations among drugs, targets and diseases by tensor decomposition for drug repositioning. BMC Bioinf. 20, 628 (2019). source
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Also, two separate GNNs may be used to learn representations of protein and small molecule graphs for predicting drug-target affinity [173].”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for therapeutics > Quantifying drug-drug and drug-target interactions
What the sources say
- Not checked yetcites [173] Choi, E., Bahadori, M. T., Song, L., Stewart, W. F. & Sun, J. GRAM: graph-based attention model for healthcare representation learning. In Proc. 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 787–795 (ACM, 2017). source
hedgedconditional- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Medical images of patients, including histopathology slides, enable clinicians to comprehensively observe the effects of a disease on the patient’s body [188].”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for healthcare > Leveraging networks for diagnostic imaging
What the sources say
- Not checked yetcites [188] Wu, T. et al. Representation learning of EHR data via graph-based medical entity embedding. In Advances in Neural Information Processing Systems Graph Representation Learning Workshop (2019).
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“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) [178].”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for therapeutics > Identifying drug-disease associations and biomarkers for complex disease
What the sources say
- Not checked yetcites [178] Zhou, Y. et al. CGC-Net: cell graph convolutional network for grading of colorectal cancer histology images. In IEEE International Conference on Computer Vision Workshops (IEEE, 2019). source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“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 [169].”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for therapeutics > Quantifying drug-drug and drug-target interactions
What the sources say
- Not checked yetcites [169] Alsentzer, E., Finlayson, S. G., Li, M. M. & Zitnik, M. Subgraph neural networks. In Proc. International Conference on Neural Information Processing Systems (eds Larochelle, H. et al.) 8017–8029 (Curran Associates, 2020).
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for therapeutics > Identifying drug-disease associations and biomarkers for complex disease
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“K-nearest neighbors, for instance, is a common used method to construct drug and target similarity networks [171].”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for therapeutics > Quantifying drug-drug and drug-target interactions
What the sources say
- Not checked yetcites [171] Barisoni, L., Lafata, K. J., Hewitt, S. M., Madabhushi, A. & Balis, U. G. Digital pathology and computational image analysis in nephropathology. Nat. Rev. Nephrol. 16, 669–685 (2020). source
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“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 [189].”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for healthcare > Leveraging networks for diagnostic imaging
What the sources say
- Not checked yetcites [189] Mao, C., Yao, L. & Luo, Y. MedGCN: medication recommendation and lab test imputation via graph convolutional networks. J. Biomed. Inform. 127, 104000 (2022). source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“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 [166].”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for therapeutics > Modeling compound molecular graphs
What the sources say
- Not checked yetcites [166] Wang, R., Li, S., Cheng, L., Wong, M. H. & Leung, K. S. Predicting associations among drugs, targets and diseases by tensor decomposition for drug repositioning. BMC Bioinf. 20, 628 (2019). source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Part of the drug discovery pipeline is minimizing adverse drug events [33, 34].”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for therapeutics > Identifying drug-disease associations and biomarkers for complex disease
What the sources say
- Not checked yetcites [33] David, L., Thakkar, A., Mercado, R. & Engkvist, O. Molecular representations in AI-driven drug discovery: a review and practical guide. J. Cheminformatics 12, 56 (2020). source
- Not checked yetcites [34] Wieder, O. et al. A compact review of molecular property prediction with graph neural networks. Drug Discov. Today. Technol. 37, 1–12 (2020). source
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“EHRs are often supplemented with other modalities, such as diseases, symptoms, molecular data, drug interactions, etc [7, 206, 211, 212].”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for healthcare > Personalizing medical knowledge networks with patient records
What the sources say
- Not checked yetcites [7] Nelson, C. A., Butte, A. J. & Baranzini, S. E. Integrating biomedical research and electronic health records to create knowledge-based biologically meaningful machine-readable embeddings. Nat. Commun. 10, 3045 (2019). source
- Not checked yetcites [206] Lanchantin, J. & Qi, Y. Graph convolutional networks for epigenetic state prediction using both sequence and 3D genome data. Bioinformatics 36, i659–i667 (2020). source
- Not checked yetcites [211] Ying, Z., Bourgeois, D., You, J., Zitnik, M. & Leskovec, J. GNNExplainer: generating explanations for graph neural networks. In Proc. International Conference on Neural Information Processing Systems (ed Wallach, H. et al.) (Curran Associates, 2019).
- Not checked yetcites [212] Agarwal, C., Lakkaraju, H. & Zitnik, M. Towards a unified framework for fair and stable graph representation learning. In Proc. Machine Learning Research (eds de Campos, C. & Maathuis, M. H.) 2114–2124 (PMLR, 2021).
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“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 [210].”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for healthcare > Personalizing medical knowledge networks with patient records
What the sources say
- Not checked yetcites [210] Machens, A. et al. An infectious disease model on empirical networks of human contact: bridging the gap between dynamic network data and contact matrices. BMC Infect. Dis. 13, 185 (2013). source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“An ST-GCN [78] is designed to utilize the underlying spatial and temporal dependencies of EHR data for generating patient diagnoses [187].”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for healthcare > Personalizing medical knowledge networks with patient records
What the sources say
- Not checked yetcites [187] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S. & Sontag, D. Learning a health knowledge graph from electronic medical records. Sci. Rep. 7, 5994 (2017). source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“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 [199].”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for healthcare > Leveraging networks for diagnostic imaging
What the sources say
- Not checked yetcites [199] Li, L. et al. A method to learn embedding of a probabilistic medical knowledge graph: algorithm development. JMIR Med. Inf. 8, e17645 (2020). source
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Meta-paths may alternatively be exploited in an EHR-derived KG to leverage higher order, semantically important relations for disease classification [214].”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for healthcare > Personalizing medical knowledge networks with patient records
What the sources say
- Not checked yetcites [214] Obermeyer, Z., Powers, B., Vogeli, C. & Mullainathan, S. Dissecting racial bias in an algorithm used to manage the health of populations. Science 366, 447–453 (2019). source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Node features for drugs and diseases can be initialized using Skipgram and then a GNN leveraging multi-layer message-passing can be applied to predict adverse drug events [211].”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for healthcare > Personalizing medical knowledge networks with patient records
What the sources say
- Not checked yetcites [211] Ying, Z., Bourgeois, D., You, J., Zitnik, M. & Leskovec, J. GNNExplainer: generating explanations for graph neural networks. In Proc. International Conference on Neural Information Processing Systems (ed Wallach, H. et al.) (Curran Associates, 2019).
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“In public health, spatial and temporal networks can model space- and time-dependent observations (e.g., disease states, susceptibility to infection [225]) to spot trends, detect anomalies, and interpret temporal dynamics.”
§ Graph Representation Learning in Biomedicine and Healthcare > Outlook
What the sources say
- Not checked yetcites [225] Tran, D. et al. Fast and precise single-cell data analysis using a hierarchical autoencoder. Nat. Commun. 12, 1029 (2021). source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“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 [8].”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for healthcare > Leveraging networks for diagnostic imaging
What the sources say
- Not checked yetcites [8] Chen, R. J. et al. Pathomic fusion: an integrated framework for fusing histopathology and genomic features for cancer diagnosis and prognosis. In IEEE Transactions on Medical Imaging Vol. 41, 757–770 (IEEE, 2022). source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Alternatively, attention mechanisms may be applied on EHR data and medical ontologies to capture the parent-child relationships [186, 204, 205].”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for healthcare > Personalizing medical knowledge networks with patient records
What the sources say
- Not checked yetcites [186] Hu, J. et al. SpaGCN: integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network. Nat. Methods 18, 1342–1351 (2021). source
- Not checked yetcites [204] Wang, T., Peng, Q., Liu, B., Liu, Y. & Wang, Y. Disease module identification based on representation learning of complex networks integrated from GWAS, eQTL summaries, and human interactome. Front. Bioeng. Biotechnol. 8, 418 (2020). source
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hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“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 [220, 221].”
§ Graph Representation Learning in Biomedicine and Healthcare > Outlook
What the sources say
- Not checked yetcites [220] Stark, C. et al. BioGRID: a general repository for interaction datasets. Nucleic Acids Res 34, D535–D539 (2006). source
- Not checked yetcites [221] Efremova, M., Vento-Tormo, M., Teichmann, S. A. & Vento-Tormo, R. Cellphonedb: inferring cell–cell communication from combined expression of multi-subunit ligand–receptor complexes. Nat. Protoc. 15, 1484–1506 (2020). source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“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) [213].”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for healthcare > Personalizing medical knowledge networks with patient records
What the sources say
- Not checked yetcites [213] Zhang, X. & Zitnik, M. GNNGuard: defending graph neural networks against adversarial attacks. In Proc. International Conference on Neural Information Processing Systems (eds Larochelle, H. et al.) 9263–9275 (Curran Associates, 2020).
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper relies on this without support.
A predominant approach to analyze single-cell RNA sequencing datasets is to transform them into gene similarity networks or cell similarity networks by correlating gene expression readouts across individual cells.
“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.”
The paper relies on this without support.
Cross-domain expertise is necessary to develop a drug with optimal binding affinity and specificity to biomarkers, maximal therapy efficacy, and minimal adverse effects.
“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.”
The paper relies on this without support.
It is critical to integrate chemical structure information, protein interactions, and clinically relevant data into predictive models for drug discovery and repurposing.
“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.”
The paper relies on this without support.
The hierarchical information inherent to ICD codes naturally lends itself to creating a rich network of medical knowledge.
“The hierarchical information inherent to ICD codes (medical ontologies) naturally lend itself to creating a rich network of medical knowledge.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“In the context of biomedical applications, these graph structures can represent various associations, including gene functions, drug responses, medical image patches, and cellular similarity [64].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [64]
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“This evaluation method aligns with the approach predominantly used in the literature12,14–16,18–20.”
From A foundation model for clinician-centered drug repurposing (2024) · cites this paper as [15]
Experimental — accuracy not yet measuredIn the citing paper
“To provide therapeutic predictions, geometric deep-learning models optimized on large medical KGs15 can match disease signatures to therapeutic candidates based on networks perturbed in disease15–18.”
From A foundation model for clinician-centered drug repurposing (2024) · cites this paper as [15]
In the citing paper
“TxGNN achieves state-of-the-art performance in drug repurposing by leveraging a network medicine principle focusing on disease–treatment mechanisms15.”
From A foundation model for clinician-centered drug repurposing (2024) · cites this paper as [15]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for genomics > Injecting single cell and spatial information into molecular networks
“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 [157].”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for genomics > Injecting single cell and spatial information into molecular networks
“cell similarity networks by correlating gene expression readouts across individual cells”
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Modeling Relational Data with Graph Convolutional Networks
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Cited by
Sorted by citations
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Advancing single-cell omics and cell-based therapeutics with quantum computing
Bose, A., Rhrissorrakrai, K., Utro, F., Parida, L., Basu, S., Born, J., Capponi, S., Chalkia, D., Chan, T. A.-T., Doğa, H., et al. (2026) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-025-00918-0 · Cited by 13
Nguyen, H.-A., Peleg, A. Y., Wisniewski, J. A., Wang, X., Wang, Z., Blakeway, L. V., Badoordeen, G. Z., Theegala, R., Doan, N. Q., Parker, M. H., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-69934-8 · Cited by 17
“Known resistomes for FCGR feature extraction were derived from the curated Antibiotic Resistance Ontology (ARO) gene list from the Comprehensive Antibiotic Resistance Data-base (CARD) (Supplementary Table 11 – 14) 65 .”
Graph representation learning method based on three-way partial order structure
Yan, E., Hao, S., Hao, S., et al. (2023) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2023.109104 · Cited by 8
Cappelletti, L., Rekerle, L., Fontana, T., Hansen, P., Casiraghi, E., Ravanmehr, V., Mungall, C., Yang, J. J., Spranger, L., Karlebach, G., et al. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbae036 · Cited by 8
“The methods are called shallow to distinguish them from graph neural networks that can generate representations for any graph element by capturing both network structure and node attributes and metadata using deep learning techniques (Li et al. 2022).”
BackgroundMethodZoo guide to network embedding
Baptista, A., Sánchez-García, R. J., Baudot, A., & Bianconi, G. (2023) · Journal of Physics Complexity · doi:10.1088/2632-072x/ad0e23 · Cited by 9
“k=1 max(0, 1− y i w k zi) + λ‖wk‖(2) , [32]”
TransformerG2G: Adaptive time-stepping for learning temporal graph embeddings using transformers
Varghese, A. J., Bora, A., Xu, M., & Karniadakis, G. E. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.12.040 · Cited by 7
Mahmud, M. I., & Banerjee, T. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag229 · Cited by 12
“Similarly, graph neural networks (GNNs) provide a superior inductive bias for modeling non-Euclidean data like 3D protein structures and interactomes, but they face unique scalability hurdles, such as over-smoothing and neighbor explosion, when applied to large-scale biological networks [6, 10].”
ConTIG: Continuous representation learning on temporal interaction graphs
Wang, Z., Yang, P., Fan, X., Xu, Y. L., Wu, Z., Pan, S., Chen, L., Zang, Y., Wang, C., & Yu, R. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106151 · Cited by 8
Knowledge Enhanced Representation Learning for Drug Discovery
Hoang, T. L., Sbodio, M. L., Galindo, M. M., Zayats, M., Fernández-Díaz, R., Valls, V., Picco, G., Berrospi, C., & López, V. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i9.28924 · Cited by 9
“An overview on graph representation learning in biomedicine can be found in (Li, Huang, and Zitnik 2022).”
BackgroundChaturvedi, R. (2024) · International World Wide Web Conference (WWW) · doi:10.1145/3589335.3651256 · Cited by 8
“While there are some recent works describing creation of patient-specific concept graphs for health outcome prediction [13], to the best of our knowledge, none of the existing works model the fine-grained temporal KGs extracted from notes which is a bottleneck for building accurate diagnostic and…”
BackgroundMihajlović, K., Malod‐Dognin, N., Ameli, C., Skupin, A., & Pržulj, N. (2024) · NAR Molecular Medicine · doi:10.1093/narmme/ugae012 · Cited by 8
“Following the idea that genes with similar embeddings participate in the same molecular mechanisms [52], we first determine which combination of the five input matrices produces gene embeddings that capture best the molecular mechanisms related to PD.”
BackgroundVenkat, A., Bhaskar, D., & Krishnaswamy, S. (2023) · Trends in Immunology · doi:10.1016/j.it.2023.05.003 · Cited by 7
Community detection for directed networks revisited using bimodularity
Cionca, A., Chan, C. H. M., & Ville, D. V. D. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2500571122 · Cited by 6
Predicting Drug–Drug Interaction via Dual-Drug Visual Representation
Xie, L., Ma, T., He, Y., Liu, Y., & Zeng, X. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c01467 · Cited by 6
Computational strategies for cross-species knowledge transfer
Yuan, H., Mancuso, C. A., Johnson, K. A., Braasch, I., & Krishnan, A. (2025) · Nature Methods · doi:10.1038/s41592-025-02931-9 · Cited by 11
Cai, J., Zhang, Y., Wang, P., & Ng, S.-K. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3646069 · Cited by 10
“GLADisofparamountimportanceacross numerousreal-worldapplications,includingfrauddetectionin financialnetworks,intrusiondetectionincommunicationsys-tems,andanomalousmoleculardiscoveryinbiomedicalanalyt-ics[8],[9],[10],[11],[12].”
Miao, G., Wu, G., Zhang, Z., Tong, Y., & Lu, B. (2023) · Electronics · doi:10.3390/electronics12132763 · Cited by 8
Gong, K., Xue, Y., Kong, L., et al. (2024) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2024.104652 · Cited by 6
Fuzzy Multiview Graph Learning on Sparse Electronic Health Records
Tang, T., Han, Z., Yu, S., Bagirov, A., & Zhang, Q. (2024) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2024.3415730 · Cited by 6
“They can effectively reflect the underlying social and health interactions among various entities, such as patients and medical events [1].”
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Maryam, Chong, K. T., & Tayara, H. (2025) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2025.145907 · Cited by 6
A novel prediction approach driven by graph representation learning for heavy metal concentrations
Hao, H., Li, P., Li, K., Shan, Y., Liu, F., Hu, N., Zhang, B., Li, M., Sang, X., Xu, X., et al. (2024) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2024.174713 · Cited by 5
Self-citationAn Overview of Open Source Deep Learning-Based Libraries for Neuroscience
Tshimanga, L. F., Pup, F. D., Corbetta, M., & Atzori, M. (2023) · Applied Sciences · doi:10.3390/app13095472 · Cited by 8
Zhang, P., Wang, Z., Sun, W., Xu, J., Zhang, W., Wu, K., Wong, L., & Li, L. (2023) · ACS Omega · doi:10.1021/acsomega.3c02763 · Cited by 5
GAN-based self-supervised message passing graph representation learning
Yang, Y., Xu, K. H., & Tang, Y. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124012 · Cited by 5
RNA knowledge-graph analysis through homogeneous embedding methods
Torgano, F., Gomez, M. S., Zignani, M., et al. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbaf109 · Cited by 5
“…the way to the application of more complex graph embedding methods aware of the heterogeneity of the underlying RNA-KG (M. Soto-Gomez et al, unpublished results for publication), and to end-to-end classification methods based on GNNs to further improve the prediction performance (Li et al. 2022).”
Hu, Y., Rehawi, G., Moyon, L., Gerstner, N., Ogris, C., Arloth, J., Bittner, F., Marsico, A., & Mueller, N. S. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.909714 · Cited by 5
Vanea, C., Džigurski, J., Rukins, V., Dodi, O., Siigur, S., Salumäe, L., Meir, K. S., Parks, W. T., Hochner‐Celnikier, D., Fraser, A., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.11.21.517353 · Cited by 5
“In recent years, the representation of cells as a spatial graph 24,43–53 has emerged as a promising way to discover new cellular phenotypes 45,49,53 , make slide-level predictions 24,44,47,52 , and hierarchically cluster cells into tissue microstructures 27,46,50,51 .”
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“Graph representation learning is such a paradigm that encodes the graph through projection to a low-dimensional vector space while maximally preserving the graph topology and node properties and has witnessed enormous success in various biomedical applications 23,26 .”
BackgroundEmbedding Methods for Electronic Health Record Research
Kauffman, J., Miotto, R., Klang, E., Costa, A. B., Norgeot, B., Žitnik, M., Shameer, K., Fei, W., Nadkarni, G. N., & Glicksberg, B. S. (2025) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-103123-094729 · Cited by 11
Self-citationA variational graph-partitioning approach to modeling protein liquid-liquid phase separation
Wang, G., Warrell, J., Zheng, S., et al. (2024) · Cell Reports Physical Science · doi:10.1016/j.xcrp.2024.102292 · Cited by 4
Re-Addressing Dementia by Network Medicine and Mechanism-Based Molecular Endotypes
Pachado, M. P., Casas, A. I., Elbatreek, M. H., Nogales, C., Güney, E., Espay, A. J., & Schmidt, H. H. H. W. (2023) · Journal of Alzheimer s Disease · doi:10.3233/jad-230694 · Cited by 4
Dual-Safety Knowledge Graph Completion for Process Industry
Wang, L., Li, H., Wang, W., Xin, G., & Wei, Y. (2024) · Electronics · doi:10.3390/electronics13010214 · Cited by 4
“KGs have found applications in a wide range of domains, from natural language processing [8–10] and information retrieval to recommendation systems [11,12] and medical healthcare [13–15].”
BackgroundDRAGON: Drug Repurposing via Graph Neural Networks with Drug and Protein Embeddings as Features
Artiñano-Muñoz, R., Santamaría, L. P., Pérez, A., & Rodríguez‐González, A. (2024) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms61543.2024.00036 · Cited by 4
“Many works have been previously developed in the intersection of biomedicine and GNNs [9], and in particular in the scope of drug and pharmacological research [20].”
BackgroundOut-of-Distribution Node Detection Based on Graph Heat Kernel Diffusion
Li, F., Wang, Y., Du, X., Li, X., & Yu, G. (2024) · Mathematics · doi:10.3390/math12182942 · Cited by 4
“These include graph convolutional networks (GCN) [1], graph autoencoders (GAE) [2], graph attention networks (GAT) [3], and graph recurrent neural networks (GRNN) [4,5].”
Simplicity within biological complexity
Pržulj, N., & Malod‐Dognin, N. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbae164 · Cited by 4
“…conceptual, modelling and computational approaches, with those based on embedding of the data points into lower-dimensional spaces, that would allow for their easier computational analyses, being expected to have ground breaking impacts in transforming the study of complex biomedical data [74, 75].”
BackgroundInfluentialMulti-View Enhancement Graph-Level Clustering Network
Li, Z., Han, R., Hu, T., et al. (2025) · IEEE Access · doi:10.1109/access.2025.3550185 · Cited by 4
“This task holds significant importance across various domains, including molecular biology (e.g., representing protein structures as graphs) [1], [2], citation networks [3], and social interactions [4], where graph structural patterns play a pivotal role in data analysis.”
Self-citationKnowledge-Based Systems in Medicine
Szolovits, P., & Alsentzer, E. (2022) · Cognitive informatics in biomedicine and healthcare · doi:10.1007/978-3-031-09108-7_4 · Cited by 4
Fatemi, M. Y., Lu, Y., Sharma, C., Feng, E., Azher, Z., Diallo, A. B., Srinivasan, G., Rosner, G. M., Pointer, K., Christensen, B., et al. (2023) · medRxiv · doi:10.1101/2023.10.09.23296701 · Cited by 8
“This approach effectively captures the relationships between different cell populations within the tissue, including tumor cells and surrounding immune and other cell subpopulations.”
Network Inference and Reconstruction in Bioinformatics
Petti, M., Punzi, C., Alfano, C., Farina, L., Astolfi, L., Paci, P., Guzzi, P. H., Castiglione, F., & Tieri, P. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95502-7.00028-2 · Cited by 3
Digital Twins as Global Learning Health and Disease Models of IndividualsPreprint
Li, X., Loscalzo, J., Mahmud, A. F., Aly, D. M., Rzhetsky, A., Žitnik, M., & Benson, M. (2024) · Preprints.org · doi:10.20944/preprints202406.0357.v1 · Cited by 4
Self-citationCross-Domain and Cross-Dimension Learning for Image-to-Graph Transformers
Berger, A. H., Lux, L., Shit, S., Ezhov, I., Kaissis, G. A., Menten, M. J., Rueckert, D., & Paetzold, J. C. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00017 · Cited by 3
“1) is a critical challenge for unlocking the full potential in many real-world applications [27].”
Advancing global dementia research through equity and inclusion
Vilor‐Tejedor, N., Danso, S., Albanus, R. D., Billingsley, K. J., Evans, T. E., Lee, L. Y., Wang, S., Jiang, J., Liu, H., Ross, J., et al. (2026) · Alzheimer s & Dementia · doi:10.1002/alz.71069 · Cited by 3
Scoping review of knowledge graph applications in biomedical and healthcare sciences
Budhdeo, S., Zhang, J., Abdulle, Y., Agapow, P., McKechnie, D. G., Archer, M., Shah, V., Forte, E., Noori, A., Žitnik, M., et al. (2025) · Wellcome Open Research · doi:10.12688/wellcomeopenres.23599.1 · Cited by 6
Self-citationEEG Correlation Analysis-guided Graph Local Enhanced Feature Learning For Emotion Recognition
Li, X., Zhuang, G., Wu, M., et al. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10890374 · Cited by 3
“However, these methods do not consider the correlation analysis between different electrodes from EEG. Research has shown that the correlation analysis plays a crucial role in emotion recognition [21].”
ERMAV: Efficient and Robust Graph Contrastive Learning via Multiadversarial Views Training
Li, W., Ng, W. W. Y., Wang, H., et al. (2025) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2025.3548175 · Cited by 3
“…[10], [11] create augmented views of the input graph and conduct instance discrimination by maximizing agreement between augmentations of the same instance while minimizing the agreement between different instances, which have demonstrated promising performance on downstream tasks [12], [13], [14].”
A Tissue-Specific and Toxicology-Focused Knowledge Graph
Tripodi, I. J., Schmidt, L., Howard, B. E., et al. (2023) · Information · doi:10.3390/info14020091 · Cited by 3
Doria-Belenguer, S., Xenos, A., Ceddia, G., Malod‐Dognin, N., & Pržulj, N. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbae075 · Cited by 3
“Recent approaches for deciphering these complex data are based on network embedding techniques (Nelson et al. 2019, Li et al. 2022).”
Shang, T., Yang, S., He, W., Zhai, T., Li, D., Hou, B., Chen, T., Moore, J. H., Ritchie, M. D., & Shen, L. (2025) · Europe PMC (PubMed Central) · Cited by 3
Enhancing Autism Diagnosis Using Federated Learning and Multimodal Data Fusion
Saraswat, M., Aman, A., Pal, V., et al. (2025) · International Conference on Communication, Security and Artificial Intelligence (ICCSAI) · doi:10.1109/iccsai64074.2025.11064052 · Cited by 2
Improving biomedical fields with knowledge graphs: Domains, Tasks, and Applications
Baazouzi, W., & Kachroudi, M. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.09.238 · Cited by 3
Learnable protein representations in computational biology for predicting drug-target affinity
Kumar, R., Romano, J. D., & Ritchie, M. D. (2026) · Journal of Cheminformatics · doi:10.1186/s13321-025-01145-7 · Cited by 2
AI for science: Progress, challenges, and perspectives
Xu, Y., Shao, Z., Liu, X., Li, Y., An, Z., Fu, C., Huang, C., Zhang, L., Gao, F., Li, J., et al. (2026) · The Innovation · doi:10.1016/j.xinn.2026.101530 · Cited by 6
Self-citationSupervised Attention Using Homophily in Graph Neural Networks
Chatzianastasis, M., Nikolentzos, G., & Vazirgiannis, M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-44216-2_47 · Cited by 2
Tertiary Lymphoid Structures Generation Through Graph-Based Diffusion
Madeira, M., Thanou, D., & Frossard, P. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-55088-1_4 · Cited by 2
Atom-Level Quantum Pretraining Enhances the Spectral Perception of Molecular Graphs in Graphormer
Fallani, A., Arjona-Medina, J. A., Chernichenko, K. Y., Ирекович, Н. Р., Wegner, J. K., & Tkatchenko, A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72381-0_7 · Cited by 2
SICGNN: structurally informed convolutional graph neural networks for protein classification
Lee, Y., Kim, E., Choi, J., & Lee, C.-H. (2024) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ad979b · Cited by 2
“The second method involves depicting proteins on graphs, wherein the amino acids or residues constituting the proteins are expressed as nodes, and the interactions or relationships between amino acids or residues are expressed as edges [9, 35].”
Applications of Machine Learning-Driven Molecular Models for Advancing Ophthalmic Precision Medicine
Kumar, R., Ong, J., Waisberg, E., Lee, R. S., Nguyen, T. A., Paladugu, P., Rivolta, M. C., Gowda, C., Janin, J. V., Saintyl, J., et al. (2025) · Bioengineering · doi:10.3390/bioengineering12020156 · Cited by 2
“However, significant barriers remain, including the challenge of integrating diverse datasets, the scarcity of large, well-annotated cohorts, and the computational complexity of such approaches [31,32].”
Wu, L., Zhou, Y., Kang, J. L., et al. (2025) · doi:10.1145/3696410.3714909 · Cited by 2
Learning global dependencies via parallelized graph transformer with hybrid attention
Duan, Y., Liu, J., He, X., et al. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.114157 · Cited by 2
Degree-Normalization Improves Random-Walk-Based Embedding Accuracy in PPI Graphs
Cappelletti, L., Taverni, S., Fontana, T., et al. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-34960-7_26 · Cited by 2
PathoGraph: A Graph-Based Method for Standardized Representation of Pathology Knowledge
Lou, P., Dong, Y., Ding, C., Wang, C., Guo, R. R., Wang, C., Wang, C., & Li, C. (2025) · Scientific Data · doi:10.1038/s41597-025-04906-z · Cited by 2
“The application of Graph ML has already shown promising results in biomedicine and healthcare 31 , such as pinpointing genetic variants that contribute to complex traits, decoding single-cell behaviors, etc.”
Revolutionary Strategy for Depicting Knowledge Graphs with Temporal Attributes
Li, S., & Li, Q. (2024) · Mathematics · doi:10.3390/math12091324 · Cited by 1
Holmes, E., Kauffman, J. A., Juliano, C. E., et al. (2024) · Pediatric Research · doi:10.1038/s41390-024-03458-z · Cited by 1
A Multi-Graph Fusion Framework for Patient Representation Learning
Liu, Y., Zhang, Z., Qin, S., & Salim, F. D. (2024) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi61247.2024.00036 · Cited by 1
Network embedding for detecting protein complexes in attributed networks
Meng, X., Wang, K., Xiang, J., et al. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10822145 · Cited by 1
Graph-Based 3-Dimensional Spatial Gene Neighborhood Networks of Single Cells in Gels and Tissues
Fang, Z., Krusen, K., Priest, H., Wang, M., Kim, S., Sriram, A., Yellanki, A., Singh, A. K., Horwitz, E. M., & Coskun, A. F. (2025) · BME Frontiers · doi:10.34133/bmef.0110 · Cited by 2
“Graphs can represent various forms of data in biological contexts, such as protein signaling networks and gene regulation networks [ 7 ].”
Wang, Z., Meng, J., Li, H., Dai, Q., Lin, X., & Luan, Y. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107265 · Cited by 1
An Express Management System With Graph Recurrent Neural Network for Estimated Time of Arrival
Su, X., Alataş, B., & Sohaib, O. (2025) · Journal of Organizational and End User Computing · doi:10.4018/joeuc.370912 · Cited by 2
“(1)Time series analysis: Employing historical traffic data for time series analysis, including autoregressive (J. Li et al., 2022), moving average (Xu et al., 2023), and autoregressive integrated moving average models (Kochetkova et al., 2023).”
Paysan, D., Radhakrishnan, A., Zhang, X., Shivashankar, G. V., & Uhler, C. (2025) · Cell Systems · doi:10.1016/j.cels.2025.101293 · Cited by 1
Robust and efficient semi-supervised learning for Ising model
Wu, D., & Liu, M. (2025) · Biometrics · doi:10.1093/biomtc/ujaf060 · Cited by 1
“Meanwhile, most existing biomedical network analyses (Hong et al., 2021; Li et al., 2022) have been based on electronic health record (EHR) as this systematized collection storing digital health information is conveniently accessible on large study cohorts.”
BackgroundJaramillo-Olivares, D., Carrasco-Ochoa, J. A., & Martínez-Trinidad, J. F. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-96255-4_6 · Cited by 1
Location Representation Learning for Urban Management with Heterogeneous Graph
Jin, X., Tao, J. X., & Fu, K. (2025) · Proceedings/IEEE International Conference on Mobile Data Management · doi:10.1109/mdm65600.2025.00034 · Cited by 1
“Molecule graphs, naturally represented as atoms and bonds, are commonly analyzed for property prediction tasks [32].”
Efficient and Robust Continual Graph Learning for Graph Classification in Biology
Zhang, D., Downer, J., Chen, C., & Wang, R. (2025) · IEEE Transactions on Signal and Information Processing over Networks · doi:10.1109/tsipn.2025.3592321 · Cited by 1
“G RAPH-STRUCTURED data have become a focal point of study in various scientific disciplines [1], [2], [3], with particular emphasis in biology, where complex molecules and the relationships between biological entities can be naturally represented as graphs [4], [5], [6].”
Yu, X., Yang, G., Zhao, Z., Li, J., Xiao, X., Zhang, X., Liu, J., & Mo, P. (2025) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5c01050 · Cited by 1
Semi-Supervised Fatty Liver Classification Using Attention-Based Graph Neural Network Models
Kim, S. Y., Wang, S., Sohn, K.-A., & Choe, E. K. (2025) · Journal of Korean Medical Science · doi:10.3346/jkms.2026.41.e30 · Cited by 1
Molecular Representations for Drug Discovery
Zhang, L., Golts, A., & Garcia, V. L. (2025) · Springer handbooks · doi:10.1007/978-3-031-81728-1_48 · Cited by 1
Zhao, S., Hu, M., & Fang, S. (2026) · Scientific Reports · doi:10.1038/s41598-026-37095-9 · Cited by 1
Ali, H., & Akhtar, M. T. (2026) · Symmetry · doi:10.3390/sym18040677 · Cited by 2
Target discovery and drug design in the era of artificial intelligence
Fleming, P., & Ivanov, A. A. (2026) · Medicinal Chemistry Research · doi:10.1007/s00044-026-03562-1 · Cited by 4
“These models integrate network topology with node-speci fi c attributes, such as gene expression, mutation pro fi les, and perturbation responses, into uni fi ed, context-aware embeddings that enable node classi fi cation, link prediction, and module detection [20, 21].”
Exact Verification of Graph Neural Networks with Incremental Constraint Solving
Liu, M., Lu, C.-H., & Kwiatkowska, M. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-26204-2_33 · Cited by 1
Epigenetic biomarkers in inflammatory bowel diseases—computational challenges and opportunities
Lee, S., Park, J., Lee, H. C., Xu, X., Slater, E., Gasparetto, M., Han, N., & Zilbauer, M. (2026) · Journal of Crohn s and Colitis · doi:10.1093/ecco-jcc/jjag022 · Cited by 1
HiGATE: hierarchical graph attention for multi-scale tissue encoder in computational pathology
Dad, I., He, J., & Shen, T. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1798040 · Cited by 2
“…thresholds, watershed segmentation) that are unable to adapt inherent tissue heterogeneity; and (3) they lack mechanisms for learnable, context-aware region formation, instead relying on prede fi ned spatial partitions that may not align with biologically meaningful tissue compartments (23, 24).”
An integrated LDA and SciBERT Model for Topic Analysis: A Case Study of Network Science
Wang, J., Lu, M., Wang, W., et al. (2026) · SAGE Open · doi:10.1177/21582440261426946 · Cited by 1
“Li et al. (2022) argued that graph representation learning would continue to advance machine learning for biomedicine and healthcare applications, including identifying genetic variations in complex traits and elucidating the role of single-cell behaviors and their impact on health.”
Novel approach to blood pressure estimation using graph-based signal relationships
Kang, Y., Yoo, S., Li, L., Cho, Y. S., & Park, C. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.110659 · Cited by 1
The role of protein embeddings for protein–protein interaction prediction with graph neural networks
Balbi, L., Sousa, R. T., Cotovio, P. G., & Pesquita, C. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag313 · Cited by 1
“Notably GRL has been successfully applied to various tasks based on PPI networks [ 18, 19] and, in fact, graphs are fast becoming the major paradigm for modeling, learning [ 20, 21], and reasoning [ 22] in biomedical systems [ 23].”
An Overview of Graph Data Missing Value Imputation
Wu, J., Tang, X., Liu, G. F., et al. (2024) · Communications in computer and information science · doi:10.1007/978-981-97-1280-9_20 · Cited by 1
AI Health Agents: Pathway2vec, ReflectE, Category Theory, and Longevity
Swan, M., 隆, 城., Roland, E., & dos Santos, R. P. (2024) · Proceedings of the AAAI Symposium Series · doi:10.1609/aaaiss.v3i1.31249 · Cited by 1
Construction and Enhancement of an RNA-Based Knowledge Graph for Discovering New RNA Drugs
Cavalleri, E., & Mesiti, M. (2024) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde60146.2024.00453 · Cited by 1
“In particular, we foresee that GRL methods, by leveraging the topology of the complex bio-medical heterogeneous graphs to embed them into compact vectorial spaces [48], could be the most promising choice to properly analyze the complex heterogeneous structure of RNA-KG.”
MethodRNA Knowledge Graph Analysis via Embedding Methods
Torgano, F., Cavalleri, E., Gliozzo, J., Stacchietti, F., Saitto, E., Mesiti, M., Casiraghi, E., & Valentini, G. (2024) · WSEAS TRANSACTIONS ON BIOLOGY AND BIOMEDICINE · doi:10.37394/23208.2024.21.30 · Cited by 1
Vu, T. T. D., Kim, J., & Jung, J. (2024) · PeerJ · doi:10.7717/peerj.18509 · Cited by 3
“A graph is a powerful data structure to represent entities and their intrinsic relationships in various domains, and biological networks ( Fasoulis, Paliouras & Kavraki, 2021 ; Li, Huang & Zitnik, 2022 ; Yi et al., 2022 ) are one of them.”
An intelligent model selection method based on graph representation learning
Yang, F., Ma, P., Li, W., et al. (2025) · SIMULATION · doi:10.1177/00375497241305832 · Cited by 1
“28 These methods have been increasingly applied across various domains, including education, 29 healthcare, 30 and transportation, 31 show-casing their versatility and effectiveness.”
Krait: A Backdoor Attack Against Graph Prompt Tuning
Song, Y., Singh, R., & Palanisamy, B. (2025) · IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) · doi:10.1109/satml64287.2025.00019 · Cited by 4
“Graph neural networks (GNNs) have achieved exceptional success in various real-world applications, such as recommendation systems [7], anomaly detection [27], drug discovery, and disease diagnosis [15].”
Discovering Fraudulent Card Transactions With Higher Order Graph Embeddings Over Neo4j
Hadjisofokelous, C., Δρακόπουλος, Γ., Sioutas, S., et al. (2025) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-96239-4_31 · Cited by 1
Cappelletti, L., Rekerle, L., Fontana, T., Hansen, P., Casiraghi, E., Ravanmehr, V., Mungall, C., Yang, J. J., Spranger, L., Karlebach, G., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.11.21.517376 · Cited by 1
“Typical ML tasks that leverage KGs involve node (entity) classification and prediction of novel relations between entities (edge prediction) [2, 3].”
BackgroundMethodGraph Neural Networks to Identify Genetic Modifiers of Rare Complex Inheritable Diseases
Niktab, E. (2023) · Open Access Victoria University of Wellington | Te Herenga Waka (Figshare) · doi:10.26686/wgtn.22714963
A software resource for large graph processing and analysis
(2023) · Nature Computational Science · doi:10.1038/s43588-023-00466-7
Graphlet-based hyperbolic embeddings capture evolutionary dynamics in genetic networksPreprint
Velasco, D. T., Windels, S. F. L., Rotkevich, M., Malod‐Dognin, N., & Pržulj, N. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.27.564419
Tools to Create Synthetic Data for Brain Images
Sindhu, S. S. S., & Vijayalakshmi, N. (2024) · Advances in data mining and database management book series · doi:10.4018/979-8-3693-1886-7.ch011
Melo, S. A., Thurler, L., Paes, A., et al. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4809341
Metadata
- Authors
- Li, M. M., Huang, K., & Zitnik, M.
- Affiliations
- Harvard University (US)
- Venue
- Nature Biomedical Engineering
- Published
- 2022-10-31
- Volume
- 6
- Issue
- 12
- Pages
- 1353-1369
- ISSN
- 2157-846X (ISSN-L)
- PMC
- PMC10699434
- OpenAlex
- W4307717106
- Semantic Scholar
- 167c4607722d10f350c320bc36a5612e72deba00
- Cited by
- 294
- References
- 218
- Citations by year
- 2026: 66 · 2025: 85 · 2024: 93 · 2023: 42 · 2022: 8
- FWCI
- 4.00
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Nature Biomedical Engineering (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- National Science Foundation (2030459, 2033384)
- AstraZeneca
- U.S. Air Force (FA8702‐15‐D‐0001)
- Harvard Data Science Initiative, Harvard University
- National Human Genome Research Institute (T32HG002295)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Keywords
- graph representation learning, network medicine, biomedical networks, graph embedding, molecular interaction networks, signaling pathways, health care system, genetic variant identification, complex traits, diagnosis and treatment, drug development
- MeSH terms
- Machine Learning, Delivery of Health Care, Humans
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