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

11 Scopus citations

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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