Machine Learning and Deep Learning for the Pharmacogenomics of Antidepressant Treatments

Abstract

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.

Fig. 1

An example of the machine learning framework for forecasting antidepressant treatment response. The machine learning model comprises two major components including a feature selection algorithm and a predictive algorithm. The feature selection algorithm produces a small subset of good features, which serves as the training dataset for subsequent analysis. The training dataset serves as the input for the predictive algorithm. The predictive algorithm estimates the prediction of antide-pressant treatment outcome from the training dataset. SNPs, single nucleotide polymorphisms.

Fig. 2

An example of the integrated approach of pharmacogenomics and neuroimaging. The idea of integrating pharmacogenomics and neuroimaging is designated as neuroimaging pharmacogenomics or neuroimaging genomics. The predictive models in integrated approach can be traditional machine learning algorithms as well as deep learning algorithms.