mlsanityData to 7 Oct 2026

Graph Representation Learning in Biomedicine

arXiv 2104.04883 ↗First posted 11 Apr 2021cs.LG cs.SI q-bio.BMcited by 79

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Abstract (arXiv)

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

Key facts. Cited by 79 papers in the map (data through 7 Oct 2026). Builds on 94 of its 268 references that are in the map. First posted on arXiv 11 Apr 2021.

Builds on

94 of 268 references.

References · 94

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2017Inductive Representation Learning on Large Graphscited by 6,284
  4. 2018How Powerful are Graph Neural Networks?cited by 4,355
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38 more
  1. 2016Variational Graph Auto-Encoderscited by 1,373
  2. 2018Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognitioncited by 1,199
  3. 2019Strategies for Pre-training Graph Neural Networkscited by 964
  4. 2014Embedding Entities and Relations for Learning and Inference in Knowledge Basescited by 929
  5. 2016Complex Embeddings for Simple Link Predictioncited by 897
  6. 2019RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Spacecited by 893
  7. 2018Representation Learning on Graphs with Jumping Knowledge Networkscited by 875
  8. 2018Hierarchical Graph Representation Learning with Differentiable Poolingcited by 786
  9. 2016Automatic chemical design using a data-driven continuous representation of moleculescited by 762
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  12. 2019Heterogeneous Graph Attention Networkcited by 732
  13. 2018Junction Tree Variational Autoencoder for Molecular Graph Generationcited by 647
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  18. 2020Directional Message Passing for Molecular Graphscited by 556
  19. 2019Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networkscited by 540
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  24. 2019MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixingcited by 442
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  35. 2019Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosiscited by 165
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50 most cited of 94 All 94 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 4; 2023: 29; 2024: 19; 2025: 17; 2026: 8.

79 papers.

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  1. 2023Uncertainty Quantification over Graph with Conformalized Graph Neural Networkscited by 79
  2. 2023GraphCare: Enhancing Healthcare Predictions with Personalized Knowledge Graphscited by 57
  3. 2023GNNDelete: A General Strategy for Unlearning in Graph Neural Networkscited by 43
  4. 2024Towards Graph Contrastive Learning: A Survey and Beyondcited by 26
  5. 2023Multimodal Data Integration for Oncology in the Era of Deep Neural Networks: A Reviewcited by 20
  6. 2023Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Modelscited by 20
  7. 2024Beyond Euclid: An Illustrated Guide to Modern Machine Learning with Geometric, Topological, and Algebraic Structurescited by 20
  8. 2023Graph Anomaly Detection in Time Series: A Surveycited by 18
  9. 2023Zero-shot causal learningcited by 17
  10. 2024A Survey of Large Language Models on Generative Graph Analytics: Query, Learning, and Applicationscited by 17
  11. 2022Multimodal Learning on Graphs for Disease Relation Extractioncited by 15
  12. 2023Improving Graph Generation by Restricting Graph Bandwidthcited by 15
38 more
  1. 2023Otter-Knowledge: benchmarks of multimodal knowledge graph representation learning from different sources for drug discoverycited by 10
  2. 2023Knowledge Graphs for the Life Sciences: Recent Developments, Challenges and Opportunitiescited by 10
  3. 2023TransformerG2G: Adaptive time-stepping for learning temporal graph embeddings using transformerscited by 9
  4. 2024GTC: GNN-Transformer Co-contrastive Learning for Self-supervised Heterogeneous Graph Representationcited by 9
  5. 2022Unsupervised Network Embedding Beyond Homophilycited by 7
  6. 2023GRAPES: Learning to Sample Graphs for Scalable Graph Neural Networkscited by 7
  7. 2023Molecular Hypergraph Neural Networkscited by 7
  8. 2024Similarity-Navigated Conformal Prediction for Graph Neural Networkscited by 7
  9. 2023Knowledge-augmented Graph Machine Learning for Drug Discovery: A Surveycited by 6
  10. 2023KG-Hub -- Building and Exchanging Biological Knowledge Graphscited by 6
  11. 2023Bi-level Contrastive Learning for Knowledge-Enhanced Molecule Representationscited by 6
  12. 2025Enhancing Trustworthiness of Graph Neural Networks with Rank-Based Conformal Trainingcited by 6
  13. 2025Biomedical Knowledge Graph: A Survey of Domains, Tasks, and Real-World Applicationscited by 6
  14. 2021ConTIG: Continuous Representation Learning on Temporal Interaction Graphscited by 5
  15. 2023RNA-KG: An ontology-based knowledge graph for representing interactions involving RNA moleculescited by 5
  16. 2023Neurosymbolic AI for Reasoning on Biomedical Knowledge Graphscited by 4
  17. 2023Self-explainable Graph Neural Network for Alzheimer's Disease And Related Dementias Risk Predictioncited by 4
  18. 2023Adversarial Attacks on Fairness of Graph Neural Networkscited by 4
  19. 2024Product Manifold Representations for Learning on Biological Pathwayscited by 4
  20. 2025Subsampling Graphs with GNN Performance Guaranteescited by 4
  21. 2025Exact Verification of Graph Neural Networks with Incremental Constraint Solvingcited by 4
  22. 2022Multimodal Learning for Multi-Omics: A Surveycited by 3
  23. 2023Graph AI in Medicinecited by 3
  24. 2024Pretraining Graph Transformers with Atom-in-a-Molecule Quantum Properties for Improved ADMET Modelingcited by 3
  25. 2024Efficient and Robust Continual Graph Learning for Graph Classification in Biologycited by 3
  26. 2021HMSG: Heterogeneous Graph Neural Network based on Metapath Subgraph Learningcited by 2
  27. 2023Zoo Guide to Network Embeddingcited by 2
  28. 2023Fair Patient Model: Mitigating Bias in the Patient Representation Learned from the Electronic Health Recordscited by 2
  29. 2023Geometric Deep Learning for Structure-Based Drug Design: A Surveycited by 2
  30. 2023Tertiary Lymphoid Structures Generation through Graph-based Diffusioncited by 2
  31. 2024Cross-domain and Cross-dimension Learning for Image-to-Graph Transformerscited by 2
  32. 2024Krait: A Backdoor Attack Against Graph Prompt Tuningcited by 2
  33. 2024DRExplainer: Quantifiable Interpretability in Drug Response Prediction with Directed Graph Convolutional Networkcited by 2
  34. 2024Leveraging Social Determinants of Health in Alzheimer's Research Using LLM-Augmented Literature Mining and Knowledge Graphscited by 2
  35. 2025DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretrainingcited by 2
  36. 2025Statistical physics analysis of graph neural networks: Approaching optimality in the contextual stochastic block modelcited by 2
  37. 2025Residual Reweighted Conformal Prediction for Graph Neural Networkscited by 2
  38. 2025Intellectual Property in Graph-Based Machine Learning as a Service: Attacks and Defensescited by 2

50 most cited of 79 All 79 (JSON)