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

arXiv: https://arxiv.org/abs/1807.00123 · HTML: https://mlsanity.com/paper/1807.00123/

## Key facts

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

## Built on by (most cited first, 30 of 43)

- [Empowering Biomedical Discovery with AI Agents](https://mlsanity.com/paper/2404.02831/) (2024)
- [Graph Representation Learning in Biomedicine](https://mlsanity.com/paper/2104.04883/) (2021)
- [Multimodal learning with graphs](https://mlsanity.com/paper/2209.03299/) (2022)
- [Are Your Sensitive Attributes Private? Novel Model Inversion Attribute Inference Attacks on Classification Models](https://mlsanity.com/paper/2201.09370/) (2022)
- [Interpretable bilinear attention network with domain adaptation improves drug-target prediction](https://mlsanity.com/paper/2208.02194/) (2022)
- [ContIG: Self-supervised Multimodal Contrastive Learning for Medical Imaging with Genetics](https://mlsanity.com/paper/2111.13424/) (2021)
- [A Survey on Explainability of Graph Neural Networks](https://mlsanity.com/paper/2306.01958/) (2023)
- [Neural Multi-Hop Reasoning With Logical Rules on Biomedical Knowledge Graphs](https://mlsanity.com/paper/2103.10367/) (2021)
- [Multimodal Learning on Graphs for Disease Relation Extraction](https://mlsanity.com/paper/2203.08893/) (2022)
- [Explainable Multilayer Graph Neural Network for Cancer Gene Prediction](https://mlsanity.com/paper/2301.08831/) (2023)
- [CustOmics: A versatile deep-learning based strategy for multi-omics integration](https://mlsanity.com/paper/2209.05485/) (2022)
- [Knowledge-augmented Graph Machine Learning for Drug Discovery: A Survey](https://mlsanity.com/paper/2302.08261/) (2023)
- [From Nodes to Narratives: Explaining Graph Neural Networks with LLMs and Graph Context](https://mlsanity.com/paper/2508.07117/) (2025)
- [Domain adaptation in small-scale and heterogeneous biological datasets](https://mlsanity.com/paper/2405.19221/) (2024)
- [Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applications](https://mlsanity.com/paper/2204.00323/) (2022)
- [Generating Post-hoc Explanations for Skip-gram-based Node Embeddings by Identifying Important Nodes with Bridgeness](https://mlsanity.com/paper/2304.12036/) (2023)
- [Machine Learning Applications for Therapeutic Tasks with Genomics Data](https://mlsanity.com/paper/2105.01171/) (2021)
- [FusionDTI: Fine-grained Binding Discovery with Token-level Fusion for Drug-Target Interaction](https://mlsanity.com/paper/2406.01651/) (2024)
- [Let it shine: Autofluorescence of Papanicolaou-stain improves AI-based cytological oral cancer detection](https://mlsanity.com/paper/2407.01869/) (2024)
- [SHAP-CAT: A interpretable multi-modal framework enhancing WSI classification via virtual staining and shapley-value-based multimodal fusion](https://mlsanity.com/paper/2410.01408/) (2024)
- [Towards deployment-centric multimodal AI beyond vision and language](https://mlsanity.com/paper/2504.03603/) (2025)
- [Deep Learning in Computational Biology: Advancements, Challenges, and Future Outlook](https://mlsanity.com/paper/2310.03086/) (2023)
- [HiGraphDTI: Hierarchical Graph Representation Learning for Drug-Target Interaction Prediction](https://mlsanity.com/paper/2404.10561/) (2024)
- [AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org](https://mlsanity.com/paper/2512.11935/) (2025)
- [Analysing Biomedical Knowledge Graphs using Prime Adjacency Matrices](https://mlsanity.com/paper/2305.10467/) (2023)
- [Human-Producible Adversarial Examples](https://mlsanity.com/paper/2310.00438/) (2023)
- [Trustworthy Enhanced Multi-view Multi-modal Alzheimer's Disease Prediction with Brain-wide Imaging Transcriptomics Data](https://mlsanity.com/paper/2406.14977/) (2024)
- [Longitudinal Ensemble Integration for sequential classification with multimodal data](https://mlsanity.com/paper/2411.05983/) (2024)
- [NeuBM: Mitigating Model Bias in Graph Neural Networks through Neutral Input Calibration](https://mlsanity.com/paper/2505.15180/) (2025)
- [Segmentation and genome annotation algorithms](https://mlsanity.com/paper/2101.00688/) (2021)

## 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.