mlsanityData to 7 Oct 2026

Machine Learning Applications for Therapeutic Tasks with Genomics Data

arXiv 2105.01171 ↗First posted 3 May 2021cs.LG q-bio.GN q-bio.QMcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 35 of its 241 references that are in the map. First posted on arXiv 3 May 2021.

Builds on

35 of 241 references.

References · 35

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23 more
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Cited by year

Citing papers per year: 2022: 3.

3 papers.

  1. 2022Self-Training: A Surveycited by 85
  2. 2022Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniquescited by 25
  3. 2022Artificial Intelligence for In Silico Clinical Trials: A Reviewcited by 6