Machine learning applications for therapeutic tasks with genomics data

Kexin Huang, Cao Xiao, Lucas M. Glass, Cathy W. Critchlow, Greg Gibson, Jimeng Sun

Research output: Contribution to journalReview articlepeer-review


Thanks to the increasing availability of genomics and other biomedical data, many machine learning algorithms have been proposed for a wide range of therapeutic discovery and development tasks. In this survey, we review the literature on machine learning applications for genomics through the lens of therapeutic development. We investigate the interplay among genomics, compounds, proteins, electronic health records, cellular images, and clinical texts. We identify 22 machine learning in genomics applications that span the whole therapeutics pipeline, from discovering novel targets, personalizing medicine, developing gene-editing tools, all the way to facilitating clinical trials and post-market studies. We also pinpoint seven key challenges in this field with potentials for expansion and impact. This survey examines recent research at the intersection of machine learning, genomics, and therapeutic development.

Original languageEnglish (US)
Article number100328
Issue number10
StatePublished - Oct 8 2021
Externally publishedYes


  • genomics
  • machine learning
  • therapeutics discovery and development

ASJC Scopus subject areas

  • Decision Sciences(all)


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