Machine learning methods to model multicellular complexity and tissue specificity

Rachel S.G. Sealfon, Aaron K. Wong, Olga G. Troyanskaya

Research output: Contribution to journalReview articlepeer-review

Abstract

Experimental approaches to study tissue specificity enable insight into the nature and organization of the cell types and tissues that constitute complex multicellular organisms. Machine learning provides a powerful tool to investigate and interpret tissue-specific experimental data. In this Review, we first provide a brief introduction to key single-cell and whole-tissue approaches that allow investigation of tissue specificity and then highlight two classes of machine-learning-based methods, which can be applied to analyse, model and interpret these experimental data. Deep learning methods can predict tissue-dependent effects of individual mutations on gene expression, alternative splicing and disease phenotypes. Network-based approaches can capture relationships between biomolecules, integrate large heterogeneous data compendia to model molecular circuits and identify tissue-specific functional relationships and regulatory connections. We conclude with an outlook to future possibilities in examining multicellular complexity by combining high-resolution, large-scale multiomics data sets and interpretable machine learning models.

Original languageEnglish (US)
Pages (from-to)717-729
Number of pages13
JournalNature Reviews Materials
Volume6
Issue number8
DOIs
StatePublished - Aug 2021

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Energy (miscellaneous)
  • Surfaces, Coatings and Films
  • Materials Chemistry

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