{
  "id": "2105.01171",
  "version": "v1",
  "published": "2021-05-03T21:20:20.000Z",
  "updated": "2021-05-03T21:20:20.000Z",
  "title": "Machine Learning Applications for Therapeutic Tasks with Genomics Data",
  "authors": [
    "Kexin Huang",
    "Cao Xiao",
    "Lucas M. Glass",
    "Cathy W. Critchlow",
    "Greg Gibson",
    "Jimeng Sun"
  ],
  "categories": [
    "cs.LG",
    "q-bio.GN",
    "q-bio.QM"
  ],
  "abstract": "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2021-05-03T21:20:20.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "machine learning applications",
      "therapeutic tasks",
      "genomics data",
      "pinpoint seven important challenges",
      "therapeutic development"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}