Recent Progress of Machine Learning in Gene Therapy.

Journal: Current gene therapy
Published Date:

Abstract

With new developments in biomedical technology, it is now a viable therapeutic treatment to alter genes with techniques like CRISPR. At the same time, it is increasingly cheaper to perform whole genome sequencing, resulting in rapid advancement in gene therapy and editing in precision medicine. Understanding the current industry and academic applications of gene therapy provides an important backdrop to future scientific developments. Additionally, machine learning and artificial intelligence techniques allow for the reduction of time and money spent in the development of new gene therapy products and techniques. In this paper, we survey the current progress of gene therapy treatments for several diseases and explore machine learning applications in gene therapy. We also discuss the ethical implications of gene therapy and the use of machine learning in precision medicine. Machine learning and gene therapy are both topics gaining popularity in various publications, and we conclude that there is still room for continued research and application of machine learning techniques in the gene therapy field.

Authors

  • Cassandra Hunt
    Department of Computer Science, Pacific Lutheran University, Tacoma, WA, United States.
  • Sandra Montgomery
    Department of Physics, Pacific Lutheran University, Tacoma, WA, United States.
  • Joshua William Berkenpas
    Department of Computer Science, Pacific Lutheran University, Tacoma, WA, United States.
  • Noel Sigafoos
    Department of Computer Science, Pacific Lutheran University, Tacoma, WA, United States.
  • John Christian Oakley
    Department of Computer Science, Pacific Lutheran University, Tacoma, WA, United States.
  • Jacob Espinosa
    Department of Mathematics, Pacific Lutheran University, Tacoma, WA, United States.
  • Nicola Justice
    Department of Mathematics, Pacific Lutheran University, Tacoma, WA 98447, United States.
  • Kiyomi Kishaba
    Department of Computer Science, Pacific Lutheran University, Tacoma, WA 98447, USA.
  • Kyle Hippe
    Department of Computer Science, Pacific Lutheran University, Tacoma, WA 98447, USA.
  • Dong Si
  • Jie Hou
    Department of Computer Science, University of Missouri, Columbia, MO, 65211, USA.
  • Hui Ding
    Medical School, Huanghe Science & Technology University, Zhengzhou 450063, PR China.
  • Renzhi Cao
    Department of Computer Science, Pacific Lutheran University, Tacoma, WA, 98447, USA.