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Machine learning applications for therapeutic tasks with genomics data.

ABSTRACT: 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.

SUBMITTER: Huang K

PROVIDER: S-EPMC8515011 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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Machine learning applications for therapeutic tasks with genomics data.

Huang Kexin K Xiao Cao C Glass Lucas M LM Critchlow Cathy W CW Gibson Greg G Sun Jimeng J

Patterns (New York, N.Y.) 20210809 10

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 ...[more]

PMID: 34693370

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