Lin Eugene, Lin Chieh-Hsin, Lane Hsien-Yuan
Department of Biostatistics, University of Washington, Seattle, WA, USA.
Department of Electrical & Computer Engineering, University of Washington, Seattle, WA, USA.
Clin Psychopharmacol Neurosci. 2021 Nov 30;19(4):577-588. doi: 10.9758/cpn.2021.19.4.577.
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.
越来越多的证据表明，机器学习和深度学习技术可以作为重度抑郁症（MDD）患者抗抑郁治疗药物基因组学的重要基础。在这篇综述中，我们重点关注使用机器学习和深度学习方法以及神经影像学和多组学数据进行药物基因组学研究的最新进展。首先，我们回顾相关的药物基因组学研究，这些研究利用众多机器学习和深度学习技术来确定MDD患者抗抑郁治疗的预测指标和潜在生物标志物。此外，我们描述了一些神经影像药物基因组学研究，这些研究利用各种机器学习方法，基于药物基因组学和神经影像学的整合研究来预测MDD患者的抗抑郁治疗结果。此外，我们总结了过去MDD患者抗抑郁治疗药物基因组学研究的局限性。最后，我们概述了对未来研究挑战和方向的讨论。鉴于神经影像学和多组学的最新进展，在药物基因组学研究中，通过采用机器学习和深度学习算法，正在识别与MDD患者抗抑郁治疗相关的各种基因组变异和生物标志物。
