Cited by 675Bioinformatics and Genomic NetworksBiomedical Text Mining and OntologiesInterdisciplinary Research and Collaboration

Machine learning for integrating data in biology and medicine: principles, practice, and opportunities

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Zitnik, Marinka, Nguyen, Francis, Wang, Bo, Leskovec, Jure, Goldenberg, Anna, Hoffman, Michael M.·2019·Information Fusion

Abstract

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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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)
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
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

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