Machine learning applications for therapeutic tasks with genomics data.

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.

⚡ This is an original paraphrased summary — not copied from the abstract. Full paper available at the source link below.

• 1 In this survey, we review the literature on machine learning applications for genomics through the lens of therapeutic development.
• 2 We investigate the interplay among genomics, compounds, proteins, electronic health records, cellular images, and clinical texts.
• 3 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.

Understanding this could lead to better treatments, improved diagnostics, or a deeper grasp of how the human body works — benefiting patient care globally.

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