mlsanityData to 14 Oct 2026

Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities

arXiv 1807.00123 ↗First posted 30 Jun 2018q-bio.QM cs.CE cs.LGcited by 43

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Abstract (arXiv)

New technologies have enabled the investigation of biology and human health at an unprecedented scale and in multiple dimensions. These dimensions include a myriad of properties describing genome, epigenome, transcriptome, microbiome, phenotype, and lifestyle. No single data type, however, can capture the complexity of all the factors relevant to understanding a phenomenon such as a disease. Integrative methods that combine data from multiple technologies have thus emerged as critical statistical and computational approaches. The key challenge in developing such approaches is the identification of effective models to provide a comprehensive and relevant systems view. An ideal method can answer a biological or medical question, identifying important features and predicting outcomes, by harnessing heterogeneous data across several dimensions of biological variation. In this Review, we describe the principles of data integration and discuss current methods and available implementations. We provide examples of successful data integration in biology and medicine. Finally, we discuss current challenges in biomedical integrative methods and our perspective on the future development of the field.

Key facts. Cited by 43 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 Jun 2018.

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Citing papers per year: 2021: 8; 2022: 6; 2023: 11; 2024: 9; 2025: 7; 2026: 2.

43 papers.

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  1. 2024Empowering Biomedical Discovery with AI Agentscited by 118
  2. 2021Graph Representation Learning in Biomedicinecited by 80
  3. 2022Multimodal learning with graphscited by 54
  4. 2022Are Your Sensitive Attributes Private? Novel Model Inversion Attribute Inference Attacks on Classification Modelscited by 43
  5. 2022Interpretable bilinear attention network with domain adaptation improves drug-target predictioncited by 39
  6. 2021ContIG: Self-supervised Multimodal Contrastive Learning for Medical Imaging with Geneticscited by 36
  7. 2023A Survey on Explainability of Graph Neural Networkscited by 29
  8. 2021Neural Multi-Hop Reasoning With Logical Rules on Biomedical Knowledge Graphscited by 25
  9. 2022Multimodal Learning on Graphs for Disease Relation Extractioncited by 15
  10. 2023Explainable Multilayer Graph Neural Network for Cancer Gene Predictioncited by 8
  11. 2022CustOmics: A versatile deep-learning based strategy for multi-omics integrationcited by 6
  12. 2023Knowledge-augmented Graph Machine Learning for Drug Discovery: A Surveycited by 6
31 more
  1. 2025From Nodes to Narratives: Explaining Graph Neural Networks with LLMs and Graph Contextcited by 6
  2. 2024Domain adaptation in small-scale and heterogeneous biological datasetscited by 5
  3. 2022Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applicationscited by 4
  4. 2023Generating Post-hoc Explanations for Skip-gram-based Node Embeddings by Identifying Important Nodes with Bridgenesscited by 4
  5. 2021Machine Learning Applications for Therapeutic Tasks with Genomics Datacited by 3
  6. 2024FusionDTI: Fine-grained Binding Discovery with Token-level Fusion for Drug-Target Interactioncited by 3
  7. 2024Let it shine: Autofluorescence of Papanicolaou-stain improves AI-based cytological oral cancer detectioncited by 3
  8. 2024SHAP-CAT: A interpretable multi-modal framework enhancing WSI classification via virtual staining and shapley-value-based multimodal fusioncited by 3
  9. 2025Towards deployment-centric multimodal AI beyond vision and languagecited by 3
  10. 2023Deep Learning in Computational Biology: Advancements, Challenges, and Future Outlookcited by 2
  11. 2024HiGraphDTI: Hierarchical Graph Representation Learning for Drug-Target Interaction Predictioncited by 2
  12. 2025AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.orgcited by 2
  13. 2023Analysing Biomedical Knowledge Graphs using Prime Adjacency Matricescited by 1
  14. 2023Human-Producible Adversarial Examplescited by 1
  15. 2024Trustworthy Enhanced Multi-view Multi-modal Alzheimer's Disease Prediction with Brain-wide Imaging Transcriptomics Datacited by 1
  16. 2024Longitudinal Ensemble Integration for sequential classification with multimodal datacited by 1
  17. 2025NeuBM: Mitigating Model Bias in Graph Neural Networks through Neutral Input Calibrationcited by 1
  18. 2021Segmentation and genome annotation algorithmscited by 0
  19. 2021A Study into patient similarity through representation learning from medical recordscited by 0
  20. 2021Application of Machine Learning in understanding plant virus pathogenesis: Trends and perspectives on emergence, diagnosis, host-virus interplay and managementcited by 0
  21. 2021Beyond Low Earth Orbit: Biomonitoring, Artificial Intelligence, and Precision Space Healthcited by 0
  22. 2023DDeMON: Ontology-based function prediction by Deep Learning from Dynamic Multiplex Networkscited by 0
  23. 2023Multi-modal Multi-kernel Graph Learning for Autism Prediction and Biomarker Discoverycited by 0
  24. 2023Studying Limits of Explainability by Integrated Gradients for Gene Expression Modelscited by 0
  25. 2023Survival Prediction from Imbalance colorectal cancer dataset using hybrid sampling methods and tree-based classifierscited by 0
  26. 2024RGDA-DDI: Residual graph attention network and dual-attention based framework for drug-drug interaction predictioncited by 0
  27. 2025Deep Contrastive Learning for Feature Alignment: Insights from Housing-Household Relationship Inferencecited by 0
  28. 2025Missing-Modality-Aware Graph Neural Network for Cancer Classificationcited by 0
  29. 2025BioNeuralNet: A Graph Neural Network based Multi-Omics Network Data Analysis Toolcited by 0
  30. 2026Benchmarking Machine Learning Models for Multi-Omics-Based Breast Cancer Predictioncited by 0
  31. 2026Responsible Integration of AI in Cancer Genomics: Barriers, Risks, and Pathways to Trustworthy Clinical Translationcited by 0