The DOI matches its Crossref record; 200 of the 218 references it cites were found in a registry or an index; 5 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
AI snapshot
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
94 Not checked yet · 23 No basis given · 12 Own interpretation · 3 Partly supported · accuracy not yet measured
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“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
What the sources say
- Partly supportedcites [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). sourceExperimental — accuracy not yet measured
The Methods section, “Graph embedding for feature learning” (page unavailable), describes learning node2vec embeddings and using cosine similarity to add edges between drugs or between targets. This supports the general embedding-then-connect-by-similarity approach, but not the attributed use of autoencoders, gene-expression readouts, or cell similarity graphs. (LLM, conf=0.84)
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.
- Partly supported
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)
plaindirect- Asserted: passes — The authors put it forward as true.
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- Supported
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
“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
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.
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None of the sources cited for this claim have been checked yet.
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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- Based: passes — The paper gives a reason why it holds.
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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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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None of the sources cited for this claim have been checked yet.
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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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
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
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
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
“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
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: unclear — It is unclear why the paper says it holds.
None of the sources cited for this claim have been checked yet.
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.
- Checkable: passes — It can be checked against evidence.
- 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. 27
- Probably not a substantial claim. 42
The cited sources support only part of this claim — read each source’s verdict below.
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
- Partly supportedcites [13] Barabási, A.-L., Gulbahce, N. & Loscalzo, J. Network medicine: a network-based approach to human disease. Nat. Rev. Genet. 12, 56–68 (2011). sourceExperimental — accuracy not yet measured
The ‘Location of disease genes within networks’ section says essential genes tend to be associated with hubs, while non-essential disease genes tend toward the functional periphery. The citing sentence broadens that second finding to non-essential genes generally, which the passage does not establish. (LLM, conf=0.86)
hedgedcorrelational- 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.
- Partly supported
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
“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.
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
“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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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. 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 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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- Based: fails — The paper may give no reason why it holds.
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.
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
“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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Probably not a substantial claim. The paper’s conclusions may not depend on it.
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
plaindirect- 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. 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
“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
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.
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
“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
plain- 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.
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
“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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- 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. 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
“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
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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Probably not a substantial claim.
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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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
“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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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says 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
“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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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
“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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Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. 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
“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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- 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.
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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Probably not a substantial claim. The paper’s conclusions may not depend on it. 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
“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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- 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. 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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- 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
“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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Probably not a substantial claim. The paper’s conclusions may not depend on it.
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.
- Checkable: passes — It can be checked against evidence.
- 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.
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.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
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.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
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.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
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.
- 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
“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.
- 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
“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.
- 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
“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.
- 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.
- 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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- 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
“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.
- Checkable: passes — It can be checked against evidence.
- 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.
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.
- 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
“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.
- 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
“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.
- Checkable: passes — It can be checked against evidence.
- 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.
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.
- Checkable: passes — It can be checked against evidence.
- 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.
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.
- 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
“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.
- 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
“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.
- 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.
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.
- Checkable: passes — It can be checked against evidence.
- 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.
- 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
“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.
- 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
“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.
- 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
“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.
- 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, 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.
- 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
“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.
- 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
“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
- Not checked yetcites [205] Dekker, J. & Misteli, T. Long-range chromatin interactions. Cold Spring Harb. Perspect. Biol. 7, a019356 (2015). 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.
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
Items 201–284 of 284
Graph Neural Network for Molecular Structure: Application in HIV Inhibitor Molecule PredictionPreprint
Nguyen, Q. (2024) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/c3mqy
Cancer survival prediction using artificial intelligence: current status and future prospects
Shaikh, H., & Ali, R. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-24028-7.00016-7
Graph-Based Representation Learning for Detection of Abnormalities in Chest X-Rays
Salahuddin, S., Ojha, A. C., Acharjya, K., et al. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-97-8043-3_114
Semi-supervised Contrastive Learning for Bioactivity Prediction using Cell Painting Image DataPreprint
Pwesombo, D. B., Beese, C. J., Schmied, C., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.26.620435
Price, A., Rasolofomanana-Rajery, S., Manpearl, K., Robertson, C. E., Krebs, N. F., Frank, D. N., Krishnan, A., Hendricks, A. E., & Tang, M. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.01.621627
“Notable metabolites classified into the dairy group included D-erythro-Sphingosine C-20, a sphingoid base derived from sphingolipids, which are abundant in dairy products 42 , and C16-OH Sulfatide, a glycosphingolipid also present in dairy products 42 ( Supplementary Tables 7 and 8 ).”
Graphlet-based hyperbolic embeddings capture evolutionary dynamics in genetic networks
Windels, S. F. L., Velasco, D. T., Rotkevich, M., Malod‐Dognin, N., & Pržulj, N. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae650 · Cited by 1
“This process is referred to as network embedding (Li et al. 2022).”
Guarascio, M., Manco, G., Mungari, S., et al. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95502-7.00127-5
Role of Artificial Intelligence in Teaching and Learning Chemical Sciences
Tanveer, S., Tanveer, M., & Tanveer, A. (2024) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9789815305180124010010 · Cited by 1
Representation Learning of Human Diseases for Indication Expansion and Investment DecisionsPreprint
Ravandi, B., Mowrey, W. R., Chatterjee, A., Khanshan, F., Haddadi, P., Ughetto, M., Barrett, I. P., Ding, W., del Angel, G., Diethe, T., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.19.624381
Hybrid Graph Representation Learning: Integrating Euclidean and Hyperbolic Space
Li, L., Luo, L., & Sun, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78189-6_18
Robust graph representation learning with asymmetric debiased contrasts
Li, W., Ng, W. W. Y., Wang, H., et al. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.126345
Kang, C., Liu, Z., & Zhang, H. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf023 · Cited by 2
“In the biomedical knowledge graph (BKG), the relationships be-tween disease, gene (or protein), and drug entities, as the high-frequency edge types, help us to understand complex biological processes (Li et al. 2022a,b, Huang et al. 2024a).”
Anomaly Detection Methods for Nonparametric Fitting Gaussian Mixture Distributions for Data Features
Zeng, J., & Tong, Y. (2024) · International Conference on Industrial IoT, Big Data and Supply Chain (IIoTBDSC) · doi:10.1109/iiotbdsc64371.2024.00044
Higher-Order Biomarkers Through Network Motif Mining: A COVID-19 Case StudyPreprint
Chahley, N., Murira, A., Nakhla, N., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.27.638877
Uncovering latent biological function associations through gene set embeddings
Huang, Y., Zhong, F., & Liu, L. (2025) · BMC Bioinformatics · doi:10.1186/s12859-025-06100-9 · Cited by 1
Castaneda, E. U., Moore, S., Bubier, J. A., Grady, S. K., Langston, M. A., Chesler, E. J., & Baker, E. J. (2025) · PLoS ONE · doi:10.1371/journal.pone.0325201
Albi, G., Dagliati, A., Vavassori, C., et al. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-95838-0_1
DGX: Uncovering General Behavior of Deep Graph Models With Model-Level Explanation
Hu, J., Liu, J., Dong, S., et al. (2025) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbbio.2025.3593917
“In the field of brain science, deep graph models have been developed to analyze brain functional networks, including the identification of brain disorders and enhancing our understanding of their functional connectivity patterns [3], [72].”
Dissecting vulnerability in information fusion process of Graph Convolutional Networks
Zhuang, S., Wu, Z., Fan, J., et al. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103536
Baykara, C. A., Ünal, A. B., Pfeifer, N., & Akgün, M. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf465
“Moreover, FDA research has predominantly focused on classification tasks, while regression-based approaches remain significantly underexplored despite their importance in biomedical applications (Lundberg et al. 2018, Poplin et al. 2018, Li et al. 2022).”
Human–AI Collaboration for Scientific Discovery
Zhao, S., Liu, Y., Wan, J., Tang, T., & Li, X. S. (2025) · Cambridge University Press eBooks · doi:10.1017/9781009587877.011
Molaei, S., Thakur, A., Clifton, L., Soltan, A. A. S., Schwab, P., Belgrave, D. C. M., Branson, K., & Clifton, D. A. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3611069 · Cited by 2
“Graph-based models, particularly Graph Neural Networks (GNNs),haveproveneffectiveinrepresentingclinicaldatawhen EHRsaretransformedintographsthatcapturecomplexrela-tionshipsbetweenpatients,events,ortemporalsequences[14], [15],[16],[17].”
GCL-ALG: graph contrastive learning with adaptive learnable view generators
Li, Y., Kang, J., Chu, Z., & Zu, B. (2025) · PeerJ Computer Science · doi:10.7717/peerj-cs.3101 · Cited by 1
Luque, L. M., Khan, A., Torrisi, G., Green, T. D., Hardman, D. T. A., Owczarek, C., Phillips, T. A., Marks, D. S., Parsons, M., Sander, C., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.06.680783
Duranthon, O., & Zdeborová, L. (2025) · Physical Review X · doi:10.1103/lfxj-hbsk
“Given the ubiquity of such data in sciences and technology, GNNs are gaining importance in their range of applications, including chemistry [1], biomedicine [2], neuroscience [3], simulating physical systems [4], particle physics [5] and solving combinatorial problems [6, 7].”
Outdoor Air Quality and Health Impact: The PANORAMA Knowledge Graph Based Approach
Karmali, N., Mamam, A., & Diallo, G. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-09321-9_3
MPRW-HGNN: A Meta-path Random Walk based Heterogeneous Graph Neural NetworkPreprint
Pei, H., Liu, Q., Xue, J., Zhang, K., Dong, K., Ding, Y. F., Zhang, L., & Lu, K. (2025) · Research Square · doi:10.21203/rs.3.rs-7782563/v1
Bajestani, G. S., Makhloughi, F., Basham, A., Evazi, E., Pour, M. R., Alizadehsani, R., & Razmpour, F. (2025) · Physical and Engineering Sciences in Medicine · doi:10.1007/s13246-025-01659-2 · Cited by 1
Singh, V., Barman, J., Kumar, S., & Jayadeva (2025) · International Conference on Data Science and Advanced Analytics · doi:10.1109/dsaa65442.2025.11247963
“By aggregating information from connected nodes, they effectively capture complex dependencies, leading to their adoption in biomedicine, materials science, and geoscience [8]–[10].”
Correlation-guided Graph Convolutional Information Aggregation Network For EEG Emotion Recognition
Zhuang, G., Li, X., Jin, J., et al. (2025) · Journal of Physics Conference Series · doi:10.1088/1742-6596/3147/1/012014
“Prior studies [10, 11] have emphasized the importance of inter-electrode correlation evaluation and the link between emotions and specific cortical regions, highlighting the need to integrate these factors for improved recognition performance.”
Tassi, S. C., Polyzos, K. D., Dimos, S. S., et al. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3609142
“For instance, representing patients as nodes and their similarities as edges in a graph enables effective modeling of data interdependencies while preserving clinically relevant information, all without compromising patient privacy [27], [28].”
Tamilselvi, M. (2025) · International Conference on Innovative Data Communication Technologies and Application (ICIDCA) · doi:10.1109/icidca66325.2025.11280436
“Whereas traditional CNNs perform well on image classification tasks, they generally have difficulty capturing complicated spatial interactions in changing environments [13].”
Hybrid SAGE-GAT and Ensemble Learning Framework for PCOS Diagnosis
J, S. D. K., Mrudula, K., reddy, S. V., Somalaram, K. B., & Sanjay, P. (2025) · International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS) · doi:10.1109/csitss67709.2025.11294626
“These methods include pooling and LSTM-based approaches, which enhance the model’s capability to handle dynamic, scalable applications in healthcare analytics [18].”
Aamer, N., Asim, M. N., Vollmer, S., & Dengel, A. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.11.693657
“Graph machine learning has been highlighted as a key emerging tool in biomedicine, with the potential to accelerate biomedical discovery [32].”
From Code to Cure: Computational Strategies in Drug Repurposing
Sijina, K. P., & Rajanikant, G. K. (2025) · Springer handbooks · doi:10.1007/978-3-031-81728-1_32
Learning Time-Varying Graph Signals via Koopman
Krishnan, S., Choi, J., & Park, J. (2026) · IEEE Transactions on Signal and Information Processing over Networks · doi:10.1109/tsipn.2025.3642225 · Cited by 2
“G RAPHS are fundamental data structures for modeling the structure and interactions within complex systems [1] across a variety of domains, including, but not limited to, social networks [2], biological systems [3], transportation networks[4],andcommunicationsystems[5].”
Comic: explainable drug repurposing via contrastive masking for interpretable connections
Aamer, N., Asim, M. N., & Dengel, A. (2026) · BMC Bioinformatics · doi:10.1186/s12859-025-06337-4 · Cited by 1
“The underlying patterns of these actions are learned by geometric deep-learning models, which are trained on extensive medical KGs ([11]).”
Luque, L. M., Khan, M. A., Torrisi, G., Green, T. D., Hardman, D. T. A., Owczarek, C., Phillips, T. A., Marks, D. S., Parsons, M., Sander, C., et al. (2026) · eLife · doi:10.7554/elife.109177
Luque, L. M., Khan, M. A., Torrisi, G., Green, T. D., Hardman, D. T. A., Owczarek, C., Phillips, T. A., Marks, D. S., Parsons, M., Sander, C., et al. (2026) · eLife · doi:10.7554/elife.109177.1
Morgan, N. K. S., & Annan-Noonoo, P. (2026) · doi:10.2196/preprints.92079
Miao, Z., Cai, X., Chen, L., et al. (2026) · IEEE Transactions on Networking · doi:10.1109/ton.2026.3662425
“…GNNs, which assume uniform node structures, HGNNs capture nuanced relationships by accommodating diverse node and edge types while preserving their unique properties, proving e ff ective across complex domains such as recommendation systems [19], social networks [20], and biomedical graphs [21].”
Fixed-point graph convolutional networks against adversarial attacks
Khan, S., Hamza, A. B., & Youssef, A. (2026) · Neural Computing and Applications · doi:10.1007/s00521-025-11752-y · Cited by 1
Zhang, W., Zeqi, X., Wu, C., et al. (2026) · Scientific Reports · doi:10.1038/s41598-026-40636-x
“Moreover, multi-scale spatial representations of gene networks tend to be more expressive than global structure 18,19 .”
Twin cross contrastive learning with multi-modality fusion for drug–target affinity prediction
Zhang, L., Wang, Z., Liu, W., Duan, X., & Dai, Q. (2026) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2026.103394
Correia, C., Ung, C. Y., Zhang, Z., Blissenbach, E., Zhu, S., Billadeau, D. D., Loh, Y., & Li, H. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27073161
Advancements in Graph Representation Learning and Applications in Computational Biology
Chatzianastasis, M. (2025) · theses.fr (ABES) · doi:10.70675/b8144805z57bfz47c2zbf7ezb00ae1597604
Dissecting medical referrals in health services: role of physician professional networks
Duarte, R. D. B., Han, Q., & Soares, C. (2026) · BMC Health Services Research · doi:10.1186/s12913-026-14099-9 · Cited by 1
“This dual capability has been applied to a range of healthcare problems, including disease classification [39], medication-related prediction tasks [40], clinical risk prediction [41], and temporal representation learning from EHRs [42, 43].”
AI-Driven Public Health Information Management and Emergency Decision-Making
邹志新, Kang, Y., Zheng, J., Cobbinah, P., Mariia, R., Xu, R., & Liu, B. (2026) · Journal of Organizational and End User Computing · doi:10.4018/joeuc.407232
Xiao, Y., Xu, X., Xiao, L., et al. (2026) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2026.3684313
“Recent studies have demonstrated that GNNs can achieve robust generalization on small biomedical datasets (samples 200) because the fixed graph topology e ff ectively constrains the hypothesis space, preventing the model from memorizing noise [25].”
Siraj, M., Natha, S. A. S., Alamer, M. M., et al. (2026) · Scientific Reports · doi:10.1038/s41598-026-49370-w
“In this representation, each pixel in the image will be a node of the graph, and any spatial or intensity-based connectivity will be recorded as an edge; thus, GNNs can perform both representations learning and very strong feature extraction 44 .”
GCE-UQ: Quantifying and Decomposing Uncertainty in Graph Counterfactual Explanations
Guo, C., Xie, S., & Zhang, X. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11460847
Kumar, R., Sporn, K., Waisberg, E., Ong, J., Paladugu, P., Sekhar, T. C., Hage, T. R., Vaja, S., Nelson, N., Masalkhi, M., et al. (2026) · Annals of Biomedical Engineering · doi:10.1007/s10439-026-04045-5
Generating explainable hypotheses for drug repurposing with graph neural networks
Perdomo-Quinteiro, P., Güney, E., & Belmonte-Hernández, A. (2026) · Scientific Reports · doi:10.1038/s41598-026-50149-2
“Additionally, GNNs can generalize across different domains, making them versatile tools for a wide range of applications, from drug repurposing in biomedical research 14,15 and supporting diagnosis in healthcare 16 to commercial product recommendation systems 17 .”
From Graph Attention to Multi-Fusion Models: Advancing Drug-Target Binding Affinity Modelling
Chanana, G. (2026) · International Conference on Advancements and Key Challenges in Green Energy and Computing (AKGEC) · doi:10.1109/akgec68790.2026.11485778
“Thus, the advancements in deep learning has aided in drug-target affinity (DTA) predictions [3].”
Guo, X., Peng, Q., Wang, W., et al. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.132923
Liu, S., Sun, M., Liu, H., Wang, Y., Zhang, F., Li, N., & Sun, P. (2026) · Research Square · doi:10.21203/rs.3.rs-9600410/v1
Knowledge Graph-Driven AI in Biohealth
Chen, C., Anandakrishnan, M., & Wu, C. H. (2026) · Delaware Journal of Public Health · doi:10.32481/djph.2026.05.17
“7,8 Meanwhile, federated data ecosystems such as the NIH Common Fund Data Ecosystem (CFDE) 9 advance FAIR (Findable, Accessible, Interoperable, Reusable) principles by harmonizing metadata across NIH Common Fund programs into integrated models, enhancing data discoverability and interoperability.”
Ravandi, C. B., Mowrey, W. R., Chatterjee, A., Khanshan, F., Haddadi, P., Mobarec, J., Lambden, S., Eliassi-Rad, T., Ricchiuto, P., & Risa, G. (2026) · bioRxiv · doi:10.64898/2026.06.15.732135 · Cited by 1
He, Y., Wei, Y., Zhi, J., Huang, C. J., Han, L., Liu, S., & Wang, L. (2026) · GigaScience · doi:10.1093/gigascience/giag075 · Cited by 1
“The development of AI architectures with inherent interpretability, such as graph neural networks informed by biological prior knowledge and symbolic reasoning systems, represents an important direction for enhancing model trustworthiness [ 239 ].”
Farrag, M. A. (2026) · Vector-Borne and Zoonotic Diseases · doi:10.1177/15303667261469037
“In addition, current DL models can predict clinical case incidence 7 – 18 days in advance with high associations in well-sampled cities (Chae et al., 2018; Li et al., 2022; Grover et al., 2025; Yang et al., 2026).”
Graph neural networks for bioinformatics
Izadkhah, H. (2026) · Elsevier eBooks · doi:10.1016/b978-0-44-344629-0.00016-1
Semantic knowledge improves molecular machine learning for chemical toxicity prediction
Albi, G., Dagliati, A., Bellazzi, R., & Romano, J. D. (2026) · iScience · doi:10.1016/j.isci.2026.116937
“20 As future work, we plan to incorporate additional structural descriptors in the baseline models, and overcome the potential pitfall of the 2D molecule representation with the 3D molecule representation.”
Xie, S., & Jing, F. S. (2026) · International Conference on Bioinformatics and Computational Biology (ICBCB) · doi:10.1109/icbcb69424.2026.11621420
“This shift enables better semantic reasoning capabilities within complex networks [8].”
Pan, X., Chen, L., Zhong, C., Huang, Q., Ye, J., Xu, Y., Pan, Y., & Zhou, L. (2026) · Chemical Papers · doi:10.1007/s11696-026-05337-9
Enhancing Graph Neural Network Explainers Using a Distribution Shift Consistency-Guided Generator
He, G., Liu, L., Zhang, Y., et al. (2026) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2026.3717880
“With GNNs increasingly being applied in high-stakes domains like health-care [4] and finance [5], explaining their predictions has become a key challenge in their deployment.”
Graph designs for deep learning–based multi-omics integration
Alif, M. N., Ahmed, K. T., Baul, S., & Zhang, W. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag410
“Despite this diversity, a common theme is that molecular biology is inherently relational [ 8]: genes co-regulate one another, proteins interact in complexes and pathways, chromatin regions co-access or loop in three dimensions, and cells communicate within tissues.”
Huang, F., Xia, Z., Ma, R., et al. (2026) · Journal of the American Statistical Association · doi:10.1080/01621459.2026.2704888
“This wealth of information has supported unsupervised representation learning methods that estimate low-dimensional embeddings without labels, enabling tasks such as patient profiling (Li et al., 2022; Landi et al., 2020) and critical event identification (Weatherhead et al., 2022).”
Liang, X., Huang, Y., & Lin, T. (2026) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2026.3714720
“In recent years, a variety of computational approaches have been proposed for predicting HSAs, including network pharmacology [2], [6], [7], graph neural networks (GNNs) [8], [9], [10], and hypergraph neural networks (HGNNs) [11], [12], [13].”
Next-Generation Nanomedicines: Integration of Nanodrug Carriers and Artificial Intelligence
Prakash, S., Madhavan, B., Kumar, S. C., Lincy, M. J. A., Nangan, S., & Pillai, B. M. (2026) · Russian Journal of Physical Chemistry A · doi:10.1134/s0036024426701591
Learning What Matters in FHIR Graph Generation for Synthetic Patient Trajectories
Bahn, M., Eryılmaz, B., Schafer, H., et al. (2026) · IEEE Access · doi:10.1109/access.2026.3730637
“Recent methods have a strong focus on explicitly modeling complex relational aspects of EHR [2], [22], [27], [34], and can be grouped into families that encode relations as similarities [11], [12], time-varying aspects [13], [14], [21], semantics [15], [16], higher-order deterministic [17] or…”
UKB-KG: Knowledge Graph for Integrating and Enhancing Biomedical Insights from the UK BiobankPreprint
Wang, Z., Wang, J., Shen, Y., et al. (2026) · medRxiv · doi:10.64898/2026.09.02.26361902
CG-MIA: A Causal Generalization framework for cross-domain graph Membership Inference Attack
Sui, P., Yang, L., Wei, Y., et al. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.117002
Alharbi, N. M. (2026) · Frontiers in Microbiology · doi:10.3389/fmicb.2026.1873220
Prior-Informed Graph Skeleton Learning for ncRNA–Drug Resistance Association Prediction
Zhu, L., & Zhang, P. (2026) · Computers · doi:10.3390/computers15090615
Fernandez-Lozano, C., Ferreiro, D., & V.-del-Río, P. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.09.08.750045
Üstündağ, H., & Doğanay, S. (2026) · Purinergic Signalling · doi:10.1007/s11302-026-10185-z
Spatially-Aware Graph Geometric Representation for Lung Nodules Detection
Guayacán, L., Leal, S., Patiño, D., et al. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-38091-3_55
A Knowledge Graph Framework for Longitudinal Congenital Heart Disease Modeling
Xu, H., Cunge, L., Ming, Z., Chen, S., Husain, M., Renella, P., Lluri, G., Finn, J. P., Hsu, W., & Nguyen, K. (2026) · JACC Advances · doi:10.1016/j.jacadv.2026.103291
Shen, L., Chen, Z., Huang, Y., Liu, J., Yuan, J., & Zhuang, S. (2026) · Environmental Science & Technology · doi:10.1021/acs.est.6c10122
Ong, C. J. N., Nazari, R., & Cruz (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-02448-w
“This graph-based view of resistome architecture provides a foundation for understanding how resistance determinants are organized and may propagate through bacterial populations [31, 178].”
Liu, S., Sun, M., Liu, H., Wang, Y., Zhang, F., Li, N., Qian, T., Zou, L., & Sun, P. (2026) · Research Square · doi:10.21203/rs.3.rs-11131457/v1
Mao, S., Meng, S., & Xiang, Y. (2026) · iScience · doi:10.1016/j.isci.2026.117693
“37,38 An extra message passing module was then introduced to assign residue-level attention scores.”
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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