The DOI matches its Crossref record; 200 of the 218 references it cites were found in a registry or an index; 4 of the 169 sources its claims cite were checked for support.
Graph representation learning in biomedicine and healthcare
Open accessAbstract
Networks—or graphs—are universal descriptors of systems of interacting elements. In biomedicine and healthcare, they can represent, for example, molecular interactions, signalling pathways, disease co-morbidities or healthcare systems. In this Perspective, we posit that representation learning can realize principles of network medicine, discuss successes and current limitations of the use of representation learning on graphs in biomedicine and healthcare, and outline algorithmic strategies that leverage the topology of graphs to embed them into compact vectorial spaces. We argue that graph representation learning will keep pushing forward machine learning for biomedicine and healthcare applications, including the identification of genetic variants underlying complex traits, the disentanglement of single-cell behaviours and their effects on health, the assistance of patients in diagnosis and treatment, and the development of safe and effective medicines. This Perspective outlines the successes and limitations of graph deep learning for biomedical and healthcare applications.
Overview
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Written from the paper’s full text.
TL;DR
This review argues that long-standing systems biology and medicine principles—such as the local hypothesis, shared components hypothesis, disease module hypothesis, and network parsimony—provide conceptual grounding for graph representation learning in biomedicine. It surveys shallow embeddings, graph neural networks, and generative graph models across molecular, genomic, therapeutic, and healthcare applications.
Key findings
- Graph representation learning learns compact vector embeddings in which nodes with similar network neighborhoods are placed close together, and it can be viewed as a differentiable engine of network biomedicine principles including the local hypothesis, shared components hypothesis, disease module hypothesis, and network parsimony.
- The reviewed methods span shallow graph embeddings (e.g., DeepWalk, node2vec, LINE, knowledge graph embeddings), graph neural networks with message passing, neighborhood aggregation, and representation updates, and deep generative graph models using variational autoencoders, generative adversarial networks, junction trees, and sequential node/edge sampling.
- Biomedical applications covered include protein structure, protein interactions, and protein function; gene expression, single-cell RNA sequencing, and spatial molecular profiling; drug-target, drug-drug, and drug-disease prediction; and patient-specific networks from histopathology images and electronic health records.
- No primary numerical results or benchmark performance numbers are reported in this review.
Methods
- Design: narrative review/survey, not a primary empirical study; it synthesizes graph representation learning approaches and biomedical applications.
- Data or sample: no new dataset is analyzed; the paper discusses published methods and application areas across molecular, genomic, therapeutic, and healthcare domains.
- Main analysis: conceptual synthesis linking graph representation learning to systems biology and medicine principles, organized into shallow graph embedding approaches, graph neural networks, and generative graph models.
Limitations
- Stated in the paper: graphs contain complex topographical structure, have no fixed node ordering or reference points, and comprise many different entity and edge types, which classic deep learning methods are unable to handle directly.
- Stated in the paper: biomedical networks are often incomplete or limited—for example, PPI networks are limited in nodes and edges due to experimental and resource constraints, gene expression data can be noisy and variational, and PPI networks cannot capture all gene regulatory activities.
- Evident from the text: as a review, it provides no empirical benchmark, systematic search protocol, or quantitative comparison; it also notes that explainability, fairness, and robustness of graph representations remain essential but unresolved needs in clinical applications.
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Claims the paper makes
95 Not checked yet · 23 No basis given · 12 Own interpretation · 2 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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- 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)
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“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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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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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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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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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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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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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.
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- Probably not a substantial claim. 42
- It may state two separate claims as one. 27
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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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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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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In the paper
“Further, essential genes are often found in hubs of a molecular network whereas non-essential genes (e.g., those associated with disease) are located on the periphery [13].”
§ Graph Representation Learning in Biomedicine and Healthcare > Introduction
What the sources say
- Not checked yetcites [13] Barabási, A.-L., Gulbahce, N. & Loscalzo, J. Network medicine: a network-based approach to human disease. Nat. Rev. Genet. 12, 56–68 (2011). source
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Such functions are optimized to embed the input network so that nodes with similar network neighborhoods are embedded close together in the embedding space, and algebraic operations performed in this learned space reflect the network’s topology.”
§ Graph Representation Learning in Biomedicine and Healthcare > Introduction
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“we put forward an observation that long-standing principles of systems biology and medicine—while often unspoken in machine learning research—provide the conceptual grounding for representation learning on graphs, explain its current successes and limitations, and even inform future advancements.”
§ Graph Representation Learning in Biomedicine and Healthcare > Abstract
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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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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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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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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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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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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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.
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In the paper
“Hence, artificial intelligence methods that produce representations can be thought of as differentiable engines of key network biomedicine principles.”
§ Graph Representation Learning in Biomedicine and Healthcare > Introduction
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Diseases are classified based on the presenting symptoms of patients, which can be caused by molecular dysfunctions, such as genetic mutations.”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for genomics
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“More concretely, representation learning specifies deep, non-linear transformation functions that map nodes to points in a compact vector space, termed embeddings.”
§ Graph Representation Learning in Biomedicine and Healthcare > Introduction
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As biomedical networks can be multimodal and massive, special consideration is needed to scale GNNs to large and heterogeneous networks.”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning > Graph neural networks
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
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In the paper
“They have been built as a hierarchical graph that GNNs then leverage to learn dependencies of the terms [126], or directly used as protein function labels [106, 127].”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for molecules > Interpreting protein functions and cellular phenotypes
What the sources say
- Not checked yetcites [126] Cao, M. et al. Going the distance for protein function prediction: a new distance metric for protein interaction networks. PLoS ONE 8, e76339 (2013). source
- Not checked yetcites [106] Hamilton, W. L., Ying, Z. & Leskovec, J. Inductive representation learning on large graphs. In Proc. International Conference on Neural Information Processing Systems (eds Guyon, I. et al.) (Curran Associates, 2017).
- Not checked yetcites [127] Dey, T. K. & Mandal, S. Protein classification with improved topological data analysis. In 18th International Workshop on Algorithms in Bioinformatics (WABI) (eds Parida, L. & Ukkonen, E.) 6:1–6:13 (Schloss Dagstuhl–Leibniz-Zentrum fuer Informatik, 2018).
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In the paper
“Similar to proteins, small compounds are modeled as 2D and 3D molecular graphs such that nodes are atoms and edges are bonds.”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for therapeutics > Modeling compound molecular graphs
What the sources say
- Not checked yetcites [83] Schlichtkrull, M. et al. Modeling relational data with graph convolutional networks. In European Semantic Web Conference (eds Gangemi, A. et al.) 593–607 (Springer, 2018). source
- Not checked yetcites [166] Wang, R., Li, S., Cheng, L., Wong, M. H. & Leung, K. S. Predicting associations among drugs, targets and diseases by tensor decomposition for drug repositioning. BMC Bioinf. 20, 628 (2019). source
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
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In the paper
“Applied to such networks, graph representation learning can impute scRNA-seq data [156, 157], predict cell clusters [157, 158], etc.”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for genomics > Injecting single cell and spatial information into molecular networks
What the sources say
- Not checked yetcites [156] Alagappan, M., Jiang, D., Denko, N. & Koong, A. C. in Tumor Microenvironment (eds Koumenis, C. et al.) 253–268 (Springer, 2016). source
- Not checked yetcites [157] Thafar, M. A. et al. DTiGEMS+: drug–target interaction prediction using graph embedding, graph mining, and similarity-based techniques. J. Cheminformatics 12, 44 (2020). source
- Not checked yetcites [158] Thafar, M. A. et al. DTi2Vec: Drug–target interaction prediction using network embedding and ensemble learning. J. Cheminformatics 13, 71 (2021). source
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
- Rests on an assumption: A predominant approach to analyze single-cell RNA sequencing datasets is to transform them into gene similarity networks or cell similarity networks by correlating gene expression readouts across individual cells.
None of the sources cited for this claim have been checked yet.
In the paper
“To this end, graph representation learning methods, such as GNNs, have been developed to learn robust and meaningful representations of molecules despite the incomplete interactome [102] and to inductively infer new edges between pairs of nodes [154].”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for genomics > Leveraging gene expression measurements
What the sources say
- Not checked yetcites [102] Yin, N. et al. Synergistic and antagonistic drug combinations depend on network topology. PLoS ONE 9, e93960 (2014). source
- Not checked yetcites [154] Coley, C. W. et al. A graph-convolutional neural network model for the prediction of chemical reactivity. Chem. Sci. 10, 370–377 (2019). source
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Spatial molecular profiling can measure both gene expression at the cellular level and location of cells in a tissue [160].”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for genomics > Injecting single cell and spatial information into molecular networks
What the sources say
- Not checked yetcites [160] Jiang, M. et al. Drug–target affinity prediction using graph neural network and contact maps. RSC Adv 10, 20701–20712 (2020). source
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Edges can also be added to indicate pairwise spatial distance between two atoms [72], or directed with information on bond angles and rotations incorporated into the molecular graph [84].”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for therapeutics > Modeling compound molecular graphs
What the sources say
- Not checked yetcites [72] Hu, Z., Dong, Y., Wang, K. & Sun, Y. Heterogeneous graph transformer. In Proc. ACM Web Conference 2704–2710 (ACM, 2020). source
- Not checked yetcites [84] Wang, X. et al. Heterogeneous graph attention network. In Proc. ACM Web Conference (eds Liu, L. & White, R.) 2022–2032 (ACM, 2019). source
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Creating cell neighborhood and spatial gene expression graphs require a distance metric, as edges are determined based on spatial proximity, while gene co-expression and molecular similarity graphs need a threshold applied on the gene expression data [35].”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for genomics > Injecting single cell and spatial information into molecular networks
What the sources say
- Not checked yetcites [35] Hetzel, L., Fischer, D. S., Günnemann, S. & Theis, F. J. Graph representation learning for single cell biology. Curr. Opin. Syst. Biol. 28, 100347 (2021). source
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“As a result, spatial transcriptomics data can be used to construct cell graphs [161], spatial gene expression graphs [162], gene co-expression networks, or molecular similarity graphs [35].”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for genomics > Injecting single cell and spatial information into molecular networks
What the sources say
- Not checked yetcites [161] Chen, L. et al. TransformerCPI: improving compound–protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments. Bioinformatics 36, 4406–4414 (2020). source
- Not checked yetcites [162] Quan, Z., Guo, Y., Lin, X., Wang, Z.-J. & Zeng, X. GraphCPI: Graph neural representation learning for compound-protein interaction. In BIBM 717–722 (2019). source
- Not checked yetcites [35] Hetzel, L., Fischer, D. S., Günnemann, S. & Theis, F. J. Graph representation learning for single cell biology. Curr. Opin. Syst. Biol. 28, 100347 (2021). source
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Modern drug discovery requires elucidating a candidate drug’s chemical structure, identifying its drug targets, quantifying its efficacy and toxicity, and detecting its potential side effects [13, 14, 32, 164].”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for therapeutics
What the sources say
- Not checked yetcites [13] Barabási, A.-L., Gulbahce, N. & Loscalzo, J. Network medicine: a network-based approach to human disease. Nat. Rev. Genet. 12, 56–68 (2011). source
- Not checked yetcites [14] Hu, J. X., Thomas, C. E. & Brunak, S. Network biology concepts in complex disease comorbidities. Nat. Rev. Genet. 17, 615–629 (2016). source
- Not checked yetcites [32] Rai, A., Shinde, P. & Jalan, S. Network spectra for drug-target identification in complex diseases: new guns against old foes. Appl. Netw. Sci. 3, 51 (2018). source
- Not checked yetcites [164] Lin, X. et al. DeepGS: Deep representation learning of graphs and sequences for drug-target binding affinity prediction. ECAI (2020).
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Each atom and bond may include features, such as atomic mass, atomic number, and bond type, to be included in the model [83, 166].”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for therapeutics > Modeling compound molecular graphs
What the sources say
- Not checked yetcites [83] Schlichtkrull, M. et al. Modeling relational data with graph convolutional networks. In European Semantic Web Conference (eds Gangemi, A. et al.) 593–607 (Springer, 2018). source
- Not checked yetcites [166] Wang, R., Li, S., Cheng, L., Wong, M. H. & Leung, K. S. Predicting associations among drugs, targets and diseases by tensor decomposition for drug repositioning. BMC Bioinf. 20, 628 (2019). source
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Also, two separate GNNs may be used to learn representations of protein and small molecule graphs for predicting drug-target affinity [173].”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for therapeutics > Quantifying drug-drug and drug-target interactions
What the sources say
- Not checked yetcites [173] Choi, E., Bahadori, M. T., Song, L., Stewart, W. F. & Sun, J. GRAM: graph-based attention model for healthcare representation learning. In Proc. 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 787–795 (ACM, 2017). source
hedgedconditional- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Medical images of patients, including histopathology slides, enable clinicians to comprehensively observe the effects of a disease on the patient’s body [188].”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for healthcare > Leveraging networks for diagnostic imaging
What the sources say
- Not checked yetcites [188] Wu, T. et al. Representation learning of EHR data via graph-based medical entity embedding. In Advances in Neural Information Processing Systems Graph Representation Learning Workshop (2019).
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“For instance, Medical Subject Headings (MeSH) terms may be used to construct a drug-disease graph, from which latent representations of drugs and diseases are learned using various graph embedding algorithms, including DeepWalk and LINE (Section 2.1) [178].”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for therapeutics > Identifying drug-disease associations and biomarkers for complex disease
What the sources say
- Not checked yetcites [178] Zhou, Y. et al. CGC-Net: cell graph convolutional network for grading of colorectal cancer histology images. In IEEE International Conference on Computer Vision Workshops (IEEE, 2019). source
hedgedconditional- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“Concretely, TDA (refer to SI Note 3) transforms experimental data into a graph where nodes represent compounds and edges indicate a level of similarity between them [169].”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for therapeutics > Quantifying drug-drug and drug-target interactions
What the sources say
- Not checked yetcites [169] Alsentzer, E., Finlayson, S. G., Li, M. M. & Zitnik, M. Subgraph neural networks. In Proc. International Conference on Neural Information Processing Systems (eds Larochelle, H. et al.) 8017–8029 (Curran Associates, 2020).
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Graph representation learning methods enables in silico modeling of drug action, which allows for more efficient ranking of candidate drugs for repurposing, such as by considering gene expression data, gene ontologies, drug similarity, and other clinically relevant data regarding side effects and indications.”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for therapeutics > Identifying drug-disease associations and biomarkers for complex disease
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“K-nearest neighbors, for instance, is a common used method to construct drug and target similarity networks [171].”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for therapeutics > Quantifying drug-drug and drug-target interactions
What the sources say
- Not checked yetcites [171] Barisoni, L., Lafata, K. J., Hewitt, S. M., Madabhushi, A. & Balis, U. G. Digital pathology and computational image analysis in nephropathology. Nat. Rev. Nephrol. 16, 669–685 (2020). source
plain- Asserted: passes — The authors put it forward as true.
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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.
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- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Intuitively, message passing steps (i.e., in GNNs (Section 2.2)) aggregate information from neighboring atoms and bonds to learn the local chemistry of each atom [166].”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for therapeutics > Modeling compound molecular graphs
What the sources say
- Not checked yetcites [166] Wang, R., Li, S., Cheng, L., Wong, M. H. & Leung, K. S. Predicting associations among drugs, targets and diseases by tensor decomposition for drug repositioning. BMC Bioinf. 20, 628 (2019). source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Part of the drug discovery pipeline is minimizing adverse drug events [33, 34].”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for therapeutics > Identifying drug-disease associations and biomarkers for complex disease
What the sources say
- Not checked yetcites [33] David, L., Thakkar, A., Mercado, R. & Engkvist, O. Molecular representations in AI-driven drug discovery: a review and practical guide. J. Cheminformatics 12, 56 (2020). source
- Not checked yetcites [34] Wieder, O. et al. A compact review of molecular property prediction with graph neural networks. Drug Discov. Today. Technol. 37, 1–12 (2020). source
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“EHRs are often supplemented with other modalities, such as diseases, symptoms, molecular data, drug interactions, etc [7, 206, 211, 212].”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for healthcare > Personalizing medical knowledge networks with patient records
What the sources say
- Not checked yetcites [7] Nelson, C. A., Butte, A. J. & Baranzini, S. E. Integrating biomedical research and electronic health records to create knowledge-based biologically meaningful machine-readable embeddings. Nat. Commun. 10, 3045 (2019). source
- Not checked yetcites [206] Lanchantin, J. & Qi, Y. Graph convolutional networks for epigenetic state prediction using both sequence and 3D genome data. Bioinformatics 36, i659–i667 (2020). source
- Not checked yetcites [211] Ying, Z., Bourgeois, D., You, J., Zitnik, M. & Leskovec, J. GNNExplainer: generating explanations for graph neural networks. In Proc. International Conference on Neural Information Processing Systems (ed Wallach, H. et al.) (Curran Associates, 2019).
- Not checked yetcites [212] Agarwal, C., Lakkaraju, H. & Zitnik, M. Towards a unified framework for fair and stable graph representation learning. In Proc. Machine Learning Research (eds de Campos, C. & Maathuis, M. H.) 2114–2124 (PMLR, 2021).
plain- Asserted: passes — The authors put it forward as true.
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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.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“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.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Alternatively, TDA can generate graphs of whole-slide images, which include tissues from various patient sources (SI Note 3), and GNNs are then used to classify the stage of colon cancer [199].”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for healthcare > Leveraging networks for diagnostic imaging
What the sources say
- Not checked yetcites [199] Li, L. et al. A method to learn embedding of a probabilistic medical knowledge graph: algorithm development. JMIR Med. Inf. 8, e17645 (2020). source
plaindirect- Asserted: passes — The authors put it forward as true.
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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.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Node features for drugs and diseases can be initialized using Skipgram and then a GNN leveraging multi-layer message-passing can be applied to predict adverse drug events [211].”
§ Graph Representation Learning in Biomedicine and Healthcare > Graph representation learning for healthcare > Personalizing medical knowledge networks with patient records
What the sources say
- Not checked yetcites [211] Ying, Z., Bourgeois, D., You, J., Zitnik, M. & Leskovec, J. GNNExplainer: generating explanations for graph neural networks. In Proc. International Conference on Neural Information Processing Systems (ed Wallach, H. et al.) (Curran Associates, 2019).
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. 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.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“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.
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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.
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.
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- 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.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“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.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
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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GNNExplainer: Generating Explanations for Graph Neural Networks.
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Self-citationLearning a Health Knowledge Graph from Electronic Medical Records
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Computational tools for prioritizing candidate genes: boosting disease gene discovery
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Graph embedding on biomedical networks: methods, applications and evaluations
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Temporal Graph Networks for Deep Learning on Dynamic Graphs
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Wan, F., Hong, L., Xiao, A., Jiang, T., & Zeng, J. (2019) · Bioinformatics · doi:10.1093/bioinformatics/bty543 · Cited by 381
Drug–target affinity prediction using graph neural network and contact maps
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“(continued from previous page) Perspective https://doi.org/10.1038/s41551-022-00942-x Additionally, two separate GNNs may be used to learn representations of protein graphs and small-molecule graphs, to predict drug– target affinity 160 .”
InfluentialHamilton, W. L. (2020) · Synthesis lectures on artificial intelligence and machine learning · doi:10.2200/s01045ed1v01y202009aim046 · Cited by 354
GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination
Shang, J., Xiao, C., Ma, T., Li, H., & Sun, J. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33011126 · Cited by 345
BERTology Meets Biology: Interpreting Attention in Protein Language Models
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Network biology concepts in complex disease comorbidities
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Constructing knowledge graphs and their biomedical applications
Nicholson, D. N., & Greene, C. S. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.05.017 · Cited by 326 · cited 1×
“…between genes 1 ; edges between genes and diseases can indicate genes that are ‘upregulated by’, ‘downregulated by’ or ‘associated with’ a disease 2 ; and edges in a knowledge network built from electronic health records (EHRs) can indicate co-occurrences of medical codes across patients 3–5 .”
Utilizing graph machine learning within drug discovery and development
Gaudelet, T., Day, B., Jamasb, A. R., Soman, J., Regep, C., Liu, G., Hayter, J. B. R., Vickers, R., Roberts, C., Tang, J., et al. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab159 · Cited by 313
Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer
Choi, E., Xu, Z., Li, Y., Dusenberry, M. W., Flores, G., Xue, E., & Dai, A. M. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i01.5400 · Cited by 305
Where Are the Disease-Associated eQTLs?
Umans, B. D., Battle, A., & Gilad, Y. (2021) · Trends in Genetics · doi:10.1016/j.tig.2020.08.009 · Cited by 296 · cited 1×
“By re-imagining genome-wide-association studies and expression-quantitative-trait-loci studies 203 as networks, biologically meaningful modules can be discovered that highlight key genes involved in the underlying mechanisms of a disease 204 .”
Biological network analysis with deep learning
Muzio, G., O’Bray, L., & Borgwardt, K. M. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa257 · Cited by 280 · cited 1×
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A Guide to Conquer the Biological Network Era Using Graph Theory
Koutrouli, M., Karatzas, E., Páez-Espino, D., & Pavlopoulos, G. A. (2020) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2020.00034 · Cited by 279
Graph representation learning: a survey
Chen, F., Wang, Y.-C., Wang, B., & Kuo, C.-C. J. (2020) · APSIPA Transactions on Signal and Information Processing · doi:10.1017/atsip.2020.13 · Cited by 277 · cited 1×
“…deep learning on structured data 17,18 ; graph neural networks 19–21 (GNNs); representation learning for homogeneous and heterogeneous graphs 22–24 , solely heterogeneous graphs 25 and dynamic graphs 26 ; data fusion 27 ; network propagation 28 ; topological data analysis 29 (TDA); and the…”
Digital pathology and computational image analysis in nephropathology
Barisoni, L., Lafata, K. J., Hewitt, S. M., Madabhushi, A., & Balis, U. G. J. (2020) · Nature Reviews Nephrology · doi:10.1038/s41581-020-0321-6 · Cited by 264 · cited 1×
“Here we highlight two types of patient data that have been successfully integrated using deep graph learning: histopathology images 8,171,172 and EHRs 173,174 .”
Long-Range Chromatin Interactions
Dekker, J., & Misteli, T. (2015) · Cold Spring Harbor Perspectives in Biology · doi:10.1101/cshperspect.a019356 · Cited by 255 · cited 1×
“Because graphs can model long-range dependencies or interactions, they can also model chromatin elements and the effects of their binding to regions across the genome 205,206 .”
Rizvi, A. H., Camara, P. G., Kandror, E. K., Roberts, T. J., Schieren, I., Maniatis, T., & Rabadan, R. (2017) · Nature Biotechnology · doi:10.1038/nbt.3854 · Cited by 248 · cited 4×
“Single-cell RNA sequencing (scRNA-seq) data lend themselves to graph representation learning for the modelling of cellular differential processes 140,141 and disease states 142 .”
InfluentialComputational network biology: Data, models, and applications
Liu, C., Ma, Y., Zhao, J., Nussinov, R., Zhang, Y., Cheng, F., & Zhang, Z. (2019) · Physics Reports · doi:10.1016/j.physrep.2019.12.004 · Cited by 230
Toward better drug discovery with knowledge graph
Zeng, X., Tu, X., Liu, Y., Fu, X., & Su, Y. (2022) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2021.09.003 · Cited by 229 · cited 2×
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node2vec: Scalable Feature Learning for Networks
Grover, A., & Leskovec, J. (2016) · PubMed · doi:10.48550/arxiv.1607.00653 · Cited by 229 · cited 2×
“Topological data analysis 156 and shallow network embedding 57 have been used to learn representations of drugs and targets.”
Quantifying the effect of experimental perturbations at single-cell resolution
Burkhardt, D. B., Stanley, J. S., Tong, A., Perdigoto, A. L., Gigante, S. A., Herold, K. C., Wolf, G., Giraldez, A. J., van Dijk, D., & Krishnaswamy, S. (2021) · Nature Biotechnology · doi:10.1038/s41587-020-00803-5 · Cited by 226 · cited 4×
“Single-cell RNA sequencing (scRNA-seq) data lend themselves to graph representation learning for the modelling of cellular differential processes 140,141 and disease states 142 .”
InfluentialCGC-Net: Cell Graph Convolutional Network for Grading of Colorectal Cancer Histology Images
Zhou, Y., Graham, S., Alemi Koohbanani, N., Shaban, M., Heng, P.-A., & Rajpoot, N. (2019) · 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW) · doi:10.1109/iccvw.2019.00050 · Cited by 225
Drug Similarity Integration Through Attentive Multi-view Graph Auto-Encoders
Ma, T., Xiao, C., Zhou, J., & Wang, F. (2018) · Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/483 · Cited by 220
A genome-wide positioning systems network algorithm for in silico drug repurposing
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“Graph theoretic techniques have fuelled many discoveries, from uncovering relationships between diseases 43–46 to repurposing drugs 6,47,48 .”
KAME: Knowledge-based Attention Model for Diagnosis Prediction in Healthcare
Ma, F., You, Q., Xiao, H., Chitta, R., Zhou, J., & Gao, J. (2018) · Proceedings of the 27th ACM International Conference on Information and Knowledge Management · doi:10.1145/3269206.3271701 · Cited by 213
Temporal modelling using single-cell transcriptomics
Ding, J., Sharon, N., & Bar-Joseph, Z. (2022) · Nature Reviews Genetics · doi:10.1038/s41576-021-00444-7 · Cited by 195
GCNG: graph convolutional networks for inferring gene interaction from spatial transcriptomics data
Yuan, Y., & Bar-Joseph, Z. (2020) · Genome Biology · doi:10.1186/s13059-020-02214-w · Cited by 194 · cited 3×
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A Systematic Survey on Deep Generative Models for Graph Generation
Guo, X., & Zhao, L. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3214832 · Cited by 194 · cited 1×
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Rhee, S., Seo, S., & Kim, S. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/490 · Cited by 191
Inferring Causal Gene Regulatory Networks from Coupled Single-Cell Expression Dynamics Using Scribe
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“For instance, edges in a regulatory network can indicate activating and inhibitory relationships between genes 1 ; edges between genes and diseases can indicate genes that are ‘upregulated by’, ‘downregulated by’ or ‘associated with’ a disease 2 ; and edges in a knowledge network built from…”
A survey on graph-based deep learning for computational histopathology
Ahmedt‐Aristizabal, D., Armin, M. A., Denman, S., Fookes, C., & Petersson, L. (2021) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2021.102027 · Cited by 165 · cited 1×
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When Does Self-Supervision Help Graph Convolutional Networks?
You, Y., Chen, T., Wang, Z., & Shen, Y. (2020) · PubMed · doi:10.48550/arxiv.2006.09136 · Cited by 152 · cited 1×
“Recent architectures have leveraged dynamic message passing 72,85,86 to deal with evolving and time-varying graphs, as well as few-shot learning 87 or self-supervised strategies 88,89 to deal with graphs that are poorly annotated and that have limited information about labels.”
Cortical graph neural network for AD and MCI diagnosis and transfer learning across populations
Chong-yaw Wee, A., Liu, C., Lee, A., Poh, J. S., Ji, H., & Qiu, A. (2019) · NeuroImage Clinical · doi:10.1016/j.nicl.2019.101929 · Cited by 146
Fast and precise single-cell data analysis using a hierarchical autoencoder
Tran, D., Nguyen, H., Tran, B., La Vecchia, C., Luu, H. N., & Nguyen, T. (2021) · Nature Communications · doi:10.1038/s41467-021-21312-2 · Cited by 145 · cited 1×
“Unlike methods for standard representation learning, GNNs can explicitly model dependencies (such as physical interactions) between proteins as well as single-cell gene expression 224,225 .”
Graph-based prediction of Protein-protein interactions with attributed signed graph embedding
Yang, F., Fan, K., Song, D., & Lin, H.-K. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-03646-8 · Cited by 144 · cited 3×
“Methods of graph representation learning are suited to leverage structural information in such multimodal datasets 100 .”
Thafar, M. A., Olayan, R. S., Ashoor, H., Albaradei, S., Bajic, V. B., Gao, X., Gojobori, T., & Essack, M. (2020) · Journal of Cheminformatics · doi:10.1186/s13321-020-00447-2 · Cited by 143
Methods for causal inference from gene perturbation experiments and validation
Meinshausen, N., Hauser, A., Mooij, J. M., Peters, J., Versteeg, P., & Bühlmann, P. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1510493113 · Cited by 140
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Yin, N., Ma, W., Pei, J., Ouyang, Q., Tang, C., & Lai, L. (2014) · PLoS ONE · doi:10.1371/journal.pone.0093960 · Cited by 139 · cited 1×
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Han, P., Yang, P. L., Zhao, P., Shang, S., Liu, Y., Zhou, J., Gao, X., & Kalnis, P. (2019) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3292500.3330912 · Cited by 138
Classification of Cancer Types Using Graph Convolutional Neural Networks
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SkipGNN: predicting molecular interactions with skip-graph networks
Huang, K., Xiao, C., Glass, L. M., Zitnik, M., & Sun, J. (2020) · Scientific Reports · doi:10.1038/s41598-020-77766-9 · Cited by 133
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Evolution of resilience in protein interactomes across the tree of life
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Self-citationEvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs
Pareja, A., Domeniconi, G., Chen, J., Ma, T., Suzumura, T., Kanezashi, H., Kaler, T., Schardl, T. B., & Leiserson, C. E. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.5984 · Cited by 128
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Deep graph embedding for prioritizing synergistic anticancer drug combinations
Jiang, P., Huang, S., Fu, Z., Sun, Z., Lakowski, T. M., & Hu, P. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.02.006 · Cited by 123
Heterogeneous Network Representation Learning
Dong, Y., Hu, Z., Wang, K., Sun, Y., & Tang, J. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/677 · Cited by 114 · cited 1×
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Graph Convolutional Neural Networks For Alzheimer’s Disease Classification
Song, T.-A., Chowdhury, S. R., Yang, F., Jacobs, H. I. L., Fakhri, G. E., Li, Q., Johnson, K. A., & Dutta, J. (2019) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2019.8759531 · Cited by 109
Representation Learning of Histopathology Images using Graph Neural Networks
Adnan, M., Kalra, S., & Tizhoosh, H. R. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00502 · Cited by 109 · cited 2×
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Network medicine framework for identifying drug-repurposing opportunities for COVID-19
Morselli Gysi, D., do Valle, Í., Zitnik, M., Ameli, A., Gan, X., Varol, O., Ghiassian, S. D., Patten, J. J., Davey, R. A., Loscalzo, J., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2025581118 · Cited by 107 · cited 3×
“Graph theoretic techniques have fuelled many discoveries, from uncovering relationships between diseases 43–46 to repurposing drugs 6,47,48 .”
Self-citationFunctions predict horizontal gene transfer and the emergence of antibiotic resistance
Zhou, H., Beltrán, J. F., & Brito, I. (2021) · Science Advances · doi:10.1126/sciadv.abj5056 · Cited by 107 · cited 1×
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Large-scale analysis of disease pathways in the human interactome
Agrawal, M., Žitnik, M., & Leskovec, J. (2017) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789813235533_0011 · Cited by 106
Self-citationKnowledge graphs and their applications in drug discovery
MacLean, F. (2021) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2021.1910673 · Cited by 105
Transforming the Language of Life
Nambiar, A., Heflin, M., Liu, S., Maslov, S., Hopkins, M., & Ritz, A. (2020) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3388440.3412467 · Cited by 104
Mao, C., Yao, L., & Luo, Y. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3153322 · Cited by 103
MARS: Markov Molecular Sampling for Multi-objective Drug DiscoveryPreprint
Xie, Y., Shi, C., Zhou, H., Yang, Y., Zhang, W., Yu, Y. H., & Li, L. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.10432 · Cited by 102
Disease Prediction via Graph Neural Networks
Sun, Z., Yin, H., Chen, H., Chen, T., Cui, L., & Yang, F. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3004143 · Cited by 98 · cited 1×
“Alternatively, attention mechanisms may be applied on EHR data and medical ontologies to capture parent–child relationships 173,190,191 .”
Network analysis reveals rare disease signatures across multiple levels of biological organization
Buphamalai, P., Kokotovic, T., Nagy, V., & Menche, J. (2021) · Nature Communications · doi:10.1038/s41467-021-26674-1 · Cited by 95 · cited 1×
“For example, following the local hypothesis, the shared-components hypothesis and the disease-module hypothesis 13 , patients with rare diseases probably have similar phenotypes and even share disease mechanisms if they are represented by nodes that have common neighbours and topology 169,170 .”
HerGePred: Heterogeneous Network Embedding Representation for Disease Gene Prediction
Yang, K., Wang, R., Liu, G., Shu, Z., Wang, N., Zhang, R., Yu, J. Q., Chen, J., Li, X., & Zhou, X. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2018.2870728 · Cited by 94
Anticancer drug synergy prediction in understudied tissues using transfer learning
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Nelson, C. A., Butte, A. J., & Baranzini, S. E. (2019) · Nature Communications · doi:10.1038/s41467-019-11069-0 · Cited by 85 · cited 4×
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InfluentialGraph Neural Network-Based Diagnosis Prediction
Li, Y., Qian, B., Zhang, X., & Liu, H. (2020) · Big Data · doi:10.1089/big.2020.0070 · Cited by 83 · cited 3×
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SIMBA: single-cell embedding along with features
Chen, H., Ryu, J., Vinyard, M. E., Lerer, A. K., & Pinello, L. (2023) · Nature Methods · doi:10.1038/s41592-023-01899-8 · Cited by 83
Huan, J., Bandyopadhyay, D., Wang, W., et al. (2005) · Journal of Computational Biology · doi:10.1089/cmb.2005.12.657 · Cited by 82 · cited 2×
“For example, a contact distance graph can be constructed where the nodes are individual residues and the edges are determined by a physical distance threshold 107 .”
Network representation learning: a systematic literature review
Li, B., & Pi, D. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-04908-5 · Cited by 78
Reconstruction of Cell-type-Specific Interactomes at Single-Cell Resolution
Mohammadi, S., Davila-Velderrain, J., & Kellis, M. (2019) · Cell Systems · doi:10.1016/j.cels.2019.10.007 · Cited by 73 · cited 1×
“GNNs can help inject cell-type-specific gene-expression information into cell-type-specific gene-interaction networks 140,218,219 .”
iScore: a novel graph kernel-based function for scoring protein–protein docking models
Geng, C., Jung, Y. G., Renaud, N., Honavar, V., Bonvin, A. M. J. J., & Xue, L. C. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz496 · Cited by 71
HyperFoods: Machine intelligent mapping of cancer-beating molecules in foods
Veselkov, K., Gonzalez, G., Aljifri, S., Galea, D., Mirnezami, R., Youssef, J., Bronstein, M. M., & Laponogov, I. (2019) · Scientific Reports · doi:10.1038/s41598-019-45349-y · Cited by 71
DTi2Vec: Drug–target interaction prediction using network embedding and ensemble learning
Thafar, M. A., Olayan, R. S., Albaradei, S., Bajic, V. B., Gojobori, T., Essack, M., & Gao, X. (2021) · Journal of Cheminformatics · doi:10.1186/s13321-021-00552-w · Cited by 70 · cited 1×
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MedGCN: Medication recommendation and lab test imputation via graph convolutional networks
Mao, C., Yao, L., & Luo, Y. (2022) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2022.104000 · Cited by 70 · cited 1×
“For example, low-dimensional embed-dings of EHR data can be generated by separately considering medical services, doctors and patients in shallow network embeddings and GNNs 188,189 .”
Chen, Z., You, Z., Guo, Z.-H., Yi, H.-C., Luo, G., & Wang, Y. (2020) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2020.00338 · Cited by 68
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“…between genes 1 ; edges between genes and diseases can indicate genes that are ‘upregulated by’, ‘downregulated by’ or ‘associated with’ a disease 2 ; and edges in a knowledge network built from electronic health records (EHRs) can indicate co-occurrences of medical codes across patients 3–5 .”
Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., & Zhou, Y. (2020) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-020-01155-x · Cited by 64 · cited 1×
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Knowledge-Based Biomedical Data Science
Callahan, T. J., Tripodi, I. J., Pielke-Lombardo, H., & Hunter, L. (2020) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-010820-091627 · Cited by 59 · cited 2×
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Topological Feature Extraction and Visualization of Whole Slide Images using Graph Neural Networks
Levy, J., Haudenschild, C., Barwick, C., Christensen, B. C., & Vaickus, L. J. (2020) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789811232701_0027 · Cited by 57
Graph representation learning for single-cell biology
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“Creating graphs of cell neighbourhood and of spatial gene expression requires a distance metric, as edges are determined on the basis of spatial proximity, whereas graphs of gene co-expression and molecular similarity need a threshold applied on the gene-expression data 35 .”
Histographs: graphs in histopathology
Anand, D., Gadiya, S., & Sethi, A. (2020) · Medical Imaging 2020: Digital Pathology · doi:10.1117/12.2550114 · Cited by 53
Ontology-based disease similarity network for disease gene prediction
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Energy‐based graph convolutional networks for scoring protein docking models
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“…contact graphs to predict intramolecular and intermolecular energies, binding affinities and quality measures for a pair of molecular complexes 114 ; and to generate ligand–protein and receptor–protein graphs to predict whether a pair of residues from the ligand and receptor proteins belongs…”
Graph2GO: a multi-modal attributed network embedding method for inferring protein functions
Fan, K., Guan, Y., & Zhang, Y. (2020) · GigaScience · doi:10.1093/gigascience/giaa081 · Cited by 50
Spage2vec: Unsupervised representation of localized spatial gene expression signatures
Partel, G., & Wählby, C. (2021) · The FEBS Journal · doi:10.1111/febs.15572 · Cited by 50 · cited 1×
“As a result, spatial transcriptomics data can be used to construct cell graphs 148 , spatial gene-expression graphs 149 , gene-co-expression networks or molecular-similarity graphs 35 .”
Kondratova, M., Czerwińska, U., Sompairac, N., Amigorena, S., Soumelis, V., Barillot, E., Zinovyev, A. Y., & Kuperstein, I. (2019) · Nature Communications · doi:10.1038/s41467-019-12270-x · Cited by 48 · cited 1×
“Single-cell transcriptomic and proteomic data capture the heterogeneity of gene expression across diverse types of cells 216,217 .”
Deep Learning of High-Order Interactions for Protein Interface Prediction
Liu, Y., Yuan, H., Cai, L., & Ji, S. (2020) · Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3394486.3403110 · Cited by 48 · cited 2×
“Interestingly, such concatenated outputs have been treated as ‘image’ inputs to convolutional neural networks 117 .”
HeteroMed: Heterogeneous Information Network for Medical Diagnosis
Hosseini, A., Chen, T., Wu, W., Sun, Y., & Sarrafzadeh, M. (2018) · Proceedings of the 27th ACM International Conference on Information and Knowledge Management · doi:10.1145/3269206.3271805 · Cited by 47
Disease state prediction from single-cell data using graph attention networks
Ravindra, N. G., Sehanobish, A., Pappalardo, J. L., Hafler, D. A., & van Dijk, D. (2020) · ACM Conference on Health, Inference, and Learning (CHIL) · doi:10.1145/3368555.3384449 · Cited by 47 · cited 2×
“Single-cell RNA sequencing (scRNA-seq) data lend themselves to graph representation learning for the modelling of cellular differential processes 140,141 and disease states 142 .”
Robustly Extracting Medical Knowledge from EHRs: A Case Study of Learning a Health Knowledge Graph
Chen, I. Y., Agrawal, M., Horng, S., & Sontag, D. A. (2019) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789811215636_0003 · Cited by 45
Network control principles for identifying personalized driver genes in cancer
Guo, W.-F., Zhang, S.-W., Zeng, T., Akutsu, T., & Chen, L. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbz089 · Cited by 44
CellVGAE: an unsupervised scRNA-seq analysis workflow with graph attention networks
Buterez, D., Bica, I., Tariq, I., Andrés-Terré, H., & Liò, P. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btab804 · Cited by 44 · cited 1×
“Alternatively, variational graph autoen-coders produce cell embeddings and interpretable attention weights, indicating what genes the model attends to when deriving an embedding for a given cell 146 .”
Lin, X. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.13902 · Cited by 43
Analysis of disease comorbidity patterns in a large-scale China population
Guo, M., Yu, Y., Wen, T., Zhang, X., Liu, B., Zhang, J., Zhang, R., Zhang, Y., & Zhou, X. (2019) · BMC Medical Genomics · doi:10.1186/s12920-019-0629-x · Cited by 42 · cited 1×
“Graph theoretic techniques have fuelled many discoveries, from uncovering relationships between diseases 43–46 to repurposing drugs 6,47,48 .”
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Incorporating biological structure into machine learning models in biomedicine
Crawford, J., & Greene, C. S. (2020) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2019.12.021 · Cited by 41
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Sparsely-connected autoencoder (SCA) for single cell RNAseq data mining
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Predicting Patient Outcomes with Graph Representation Learning
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GraphCPI: Graph Neural Representation Learning for Compound-Protein Interaction
Quan, Z., Guo, Y., Lin, X., Wang, Z.-J., & Zeng, X. (2019) · 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm47256.2019.8983267 · Cited by 35
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DeepGOA: Predicting Gene Ontology Annotations of Proteins via Graph Convolutional Network
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Sumathipala, M. G., Maiorino, E., Weiss, S. T., & Sharma, A. (2019) · Frontiers in Physiology · doi:10.3389/fphys.2019.00888 · Cited by 30 · cited 1×
“Graph theoretic techniques have fuelled many discoveries, from uncovering relationships between diseases 43–46 to repurposing drugs 6,47,48 .”
Towards Explainable Graph Representations in Digital PathologyPreprint
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“Because interpretability is critical for models that generate patient predictions, post-hoc graph-pruning optimization may be performed on a cell graph generated from a histopathology image, to define subgraphs that explain the original cell-graph analysis 179 .”
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Wang, T., Peng, Q., Liu, B., Liu, Y., & Wang, Y. (2020) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2020.00418 · Cited by 27 · cited 1×
“By re-imagining genome-wide-association studies and expression-quantitative-trait-loci studies 203 as networks, biologically meaningful modules can be discovered that highlight key genes involved in the underlying mechanisms of a disease 204 .”
Gene Prioritization by Compressive Data Fusion and Chaining
Žitnik, M., Nam, E. A., Dinh, C. Q., Kuspa, A., Shaulsky, G., & Zupan, B. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004552 · Cited by 25 · cited 1×
“Characterizing a protein’s function in specific biological contexts is a challenging and experimentally intensive task 119,120 .”
Self-citationA Method to Learn Embedding of a Probabilistic Medical Knowledge Graph: Algorithm Development
Li, L., Wang, P., Wang, Y., Wang, S., Yan, J., Jiang, J., Tang, B., Wang, C., & Liu, Y. (2020) · JMIR Medical Informatics · doi:10.2196/17645 · Cited by 25
Wu, S., Chen, D., & Snyder, M. P. (2022) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2021.102101 · Cited by 25 · cited 2×
“As graph representation learning has aided the mapping of genotypes to phenotypes, leveraging it for fine-scale mapping of genetic variants appears promising 202 .”
Yao, H., Guan, J., & Liu, T. (2020) · Journal of Bioinformatics and Computational Biology · doi:10.1142/s0219720020400107 · Cited by 23 · cited 1×
“Similar graph convolution methods can also be used to remove less credible interactions, thereby constructing a more reliable protein–protein interaction network 118 .”
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Supervised Approaches for Protein Function Prediction by Topological Data Analysis
Martino, A., Rizzi, A., & Mascioli, F. M. F. (2018) · doi:10.1109/ijcnn.2018.8489307 · Cited by 22 · cited 1×
“…for predicting protein function 126 ; the use of the theory of topological persistence to compute signatures of a protein on the basis of its 3D structure 127 ; and the application of TDA to extract features from protein-contact networks created from 3D coordinates 128 (Supplementary Note 3).”
Effectively Identifying Compound-Protein Interaction Using Graph Neural Representation
Lin, X., Quan, Z., Wang, Z.-J., Guo, Y. P., Zeng, X., & Yu, P. S. (2022) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2022.3198003 · Cited by 22
Hovenga, V., Oluwadare, O., & Kalita, J. (2021) · doi:10.1101/2021.11.29.470405 · Cited by 21
Hovenga, V., Kalita, J. K., & Oluwadare, O. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.12.051 · Cited by 20
A Graph Feature Auto-Encoder for the prediction of unobserved node features on biological networks
Hasibi, R., & Michoel, T. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04447-3 · Cited by 19 · cited 1×
“Additionally, gene-interaction networks that leverage transcriptomic data 105,125 can capture context-specific interactions between genes (Fig.”
A Topological Data Analysis Approach on Predicting Phenotypes from Gene Expression Data
Mandal, S., Guzmán-Sáenz, A., Haiminen, N., Basu, S., & Parida, L. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-42266-0_14 · Cited by 17 · cited 1×
“Topo-logical landscapes present in gene-expression data can be vectorized Perspective https://doi.org/10.1038/s41551-022-00942-x and fed into a GCN to classify the disease type 132 .”
InfluentialGraph convolutional networks for epigenetic state prediction using both sequence and 3D genome data
Lanchantin, J., & Qi, Y. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa793 · Cited by 17
Towards perturbation prediction of biological networks using deep learning
Li, D., & Gao, J. (2019) · Scientific Reports · doi:10.1038/s41598-019-48391-y · Cited by 16 · cited 1×
“Yet topology-based methods can capture and leverage the dynamics of biological systems to enrich existing protein–protein interaction networks 116 .”
Dynamic Functional Connectivity and Graph Convolution Network for Alzheimer's Disease Classification
An, X., Zhou, Y., Di, Y., & Ming, D. (2020) · 2020 7th International Conference on Biomedical and Bioinformatics Engineering · doi:10.1145/3444884.3444885 · Cited by 15 · cited 1×
“GNNs have been used to classify the progression of Alzheimer’s disease from magnetic resonance images that are converted into graphs 181–183 .”
Network spectra for drug-target identification in complex diseases: new guns against old foes
Rai, A., Shinde, P., & Jalan, S. (2018) · Applied Network Science · doi:10.1007/s41109-018-0107-y · Cited by 12 · cited 2×
“…17,18 ; graph neural networks 19–21 (GNNs); representation learning for homogeneous and heterogeneous graphs 22–24 , solely heterogeneous graphs 25 and dynamic graphs 26 ; data fusion 27 ; network propagation 28 ; topological data analysis 29 (TDA); and the creation of biomedical networks 9,29–32 .”
Analysis of Disease Comorbidity Patterns in a Large-Scale China Population
Guo, M., Yu, Y., Wen, T., et al. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-95933-7_34 · Cited by 11
Alagappan, M., Jiang, D., Denko, N., & Koong, A. C. (2016) · Advances in Experimental Medicine and Biology · doi:10.1007/978-3-319-26666-4_15 · Cited by 10
Mixed Pooling Multi-View Attention Autoencoder for Representation Learning in HealthcarePreprint
Chowdhury, S., Zhang, C., Yu, P. S., & Luo, Y. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.06456 · Cited by 10 · cited 1×
“A mixed pooling multi-view self-attention autoencoder can generate patient representations for predicting either a patient’s risk of developing a disease in a future visit, or the diagnostic codes of the next visit 193 .”
Fan, K., & Zhang, Y. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.00807 · Cited by 7
Representation Learning of EHR Data via Graph-Based Medical Entity EmbeddingPreprint
Wu, T., Wang, Y., Wang, Y., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.02574 · Cited by 5
Xie, Y., Peng, J., & Zhou, Y. (2019) · doi:10.21203/rs.2.18936/v1 · Cited by 5
Dynamic Functional Connectivity and Graph Convolution Network for Alzheimer's Disease ClassificationPreprint
An, X., Zhou, Y., Di, Y., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.13510 · Cited by 3 · cited 1×
“GNNs have been used to classify the progression of Alzheimer’s disease from magnetic resonance images that are converted into graphs 181–183 .”
Blevins, A. S., & Bassett, D. S. (2021) · Springer eBooks · doi:10.1007/978-3-319-57072-3_87 · Cited by 2
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“We also envision integrating patient information with medical KGs to provide personalized drug-repurposing predictions.”
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“Graph (network) representation has been widely applied in systems biology to represent biological organizations and interactions[83].”
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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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-09, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-09 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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