  Thanks to the increasing availability of genomics and other biomedical data,
many machine learning approaches 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 (EHR), cellular images, and
clinical texts. We identify twenty-two machine learning in genomics
applications across the entire therapeutics pipeline, from discovering novel
targets, personalized medicine, developing gene-editing tools all the way to
clinical trials and post-market studies. We also pinpoint seven important
challenges in this field with opportunities for expansion and impact. This
survey overviews recent research at the intersection of machine learning,
genomics, and therapeutic development.
