Machine learning applications for therapeutic tasks with genomics data.

🤖 Plain-English Summary

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. We also pinpoint seven key challenges in this field with potentials for expansion and impact.

🔑 Key Findings

• 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.

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