Machine learning for integrating data in biology and medicine: principles, practice, and opportunities
Open accessAbstract
New technologies have enabled the investigation of biology and human health at an unprecedented scale and in multiple dimensions. These dimensions include myriad 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.
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Integrating patient data from various sources can improve explainability of clinical decisions.
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Emotion recognition using multi-modal data and machine learning techniques: A tutorial and review
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Artificial intelligence for multimodal data integration in oncology
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- Authors
- Zitnik, M., Nguyen, F., Wang, B., Leskovec, J., Goldenberg, A., & Hoffman, M. M.
- Affiliations
- University of Toronto (CA)
- Chan Zuckerberg Biohub San Francisco (US)
- Corresponding author
- Leskovec, Jure
- Goldenberg, Anna
- Hoffman, Michael M.
- Venue
- Information Fusion
- Publisher
- Elsevier BV
- Published
- 2019-10
- Volume
- 50
- Pages
- 71-91
- ISSN
- 1566-2535 (ISSN-L)
- PMC
- PMC6242341
- OpenAlex
- W2810986024
- Cited by
- 675
- References
- 406
- Citations by year
- 2026: 120 · 2025: 114 · 2024: 97 · 2023: 82 · 2022: 78 · 2021: 80 · 2020: 62 · 2019: 39 · 2018: 2 · 2017: 1
- FWCI
- 16.19
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Information Fusion (acceptedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (IIS-1149837, NIH BD2K U54EB020405)
- Natural Sciences and Engineering Research Council of Canada (RGPIN-2015-03948)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Social Sciences › Decision Sciences › Information Systems and Management › Interdisciplinary Research and Collaboration
- Keywords
- data integration, multi-omics, machine learning, heterogeneous biological data, genomics, epigenomics, transcriptomics, microbiome, phenotypes, disease prediction, feature selection, systems biology
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.
Statistics and arithmetic are recomputed, not judged.
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