Demystifying the black box: A survey on explainable artificial intelligence (XAI) in bioinformatics

The widespread adoption of Artificial Intelligence (AI) and machine learning (ML) tools across various domains has showcased their remarkable capabilities and performance. Black-box AI models raise concerns about decision transparency and user confidence. Therefore, explainable AI (XAI) and explainability techniques have rapidly emerged in recent years. This paper aims to review existing works on explainability techniques in bioinformatics, with a particular focus on omics and imaging. We seek to analyze the growing demand for XAI in bioinformatics, identify current XAI approaches, and highlight their limitations. Our survey emphasizes the specific needs of both bioinformatics applications and users when…

• IU
  Indiana University
• NI
  National Institutes of Health

  Awards: R35GM151089, R01LM013771, U24AA026969
• NI
  National Institute on Alcohol Abuse and Alcoholism

  Awards: R21AA031370, U24AA026969
• NO
  NIH Office of the Director
• NC
  National Cancer Institute

  Award: P30CA 082709
• NI
  National Institute of General Medical Sciences

  Award: R35GM151089
• UN
  U.S. National Library of Medicine

  Award: R01LM013771