Clin Psychopharmacol Neurosci 2021; 19(4): 577-588
Machine Learning and Deep Learning for the Pharmacogenomics of Antidepressant Treatments
Eugene Lin1,2,3, Chieh-Hsin Lin3,4,5, Hsien-Yuan Lane3,6,7,8
1Department of Biostatistics, 2Department of Electrical & Computer Engineering, University of Washington, Seattle, WA, USA, 3Graduate Institute of Biomedical Sciences, China Medical University, Taichung, 4Department of Psychiatry, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, 5School of Medicine, Chang Gung University, Taoyuan, 6Department of Psychiatry, 7Brain Disease Research Center, China Medical University Hospital, 8Department of Psychology, College of Medical and Health Sciences, Asia University, Taichung, Taiwan
Correspondence to: Chieh-Hsin Lin
Department of Psychiatry, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, No.123, Dapi Road, Niaosong District, Kaohsiung 83301, Taiwan
Hsien-Yuan Lane
Department of Psychiatry, China Medical University Hospital, No. 2, Yude Road, North District, Taichung 40447, Taiwan
Received: February 9, 2021; Accepted: April 10, 2021; Published online: November 30, 2021.
© The Korean College of Neuropsychopharmacology. All rights reserved.

This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
A growing body of evidence now proposes that machine learning and deep learning techniques can serve as a vital foundation for the pharmacogenomics of antidepressant treatments in patients with major depressive disorder (MDD). In this review, we focus on the latest developments for pharmacogenomics research using machine learning and deep learning approaches together with neuroimaging and multi-omics data. First, we review relevant pharmacogenomics studies that leverage numerous machine learning and deep learning techniques to determine treatment prediction and potential biomarkers for antidepressant treatments in MDD. In addition, we depict some neuroimaging pharmacogenomics studies that utilize various machine learning approaches to predict antidepressant treatment outcomes in MDD based on the integration of research on pharmacogenomics and neuroimaging. Moreover, we summarize the limitations in regard to the past pharmacogenomics studies of antidepressant treatments in MDD. Finally, we outline a discussion of challenges and directions for future research. In light of latest advancements in neuroimaging and multi-omics, various genomic variants and biomarkers associated with antidepressant treatments in MDD are being identified in pharmacogenomics research by employing machine learning and deep learning algorithms.
Keywords: Antidepressive agents; Artificial intelligence; Deep learning; Genomics; Machine learning; Neuroimaging

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  • National Health Research Institutes, Taiwan
      NHRI- EX109-10731NI
  • Ministry of Science and Technology in Taiwan
      MOST 109-2622-B-039-001-CC2; 109-2314-B-039-001; 109-2314-B-039-039-MY3
  • Taiwan Ministry of Health and Welfare Clinical Trial and Research Center of Excellence

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