Abstract
In the wake of recent advances in machine learning research, the study of pharmacogenomics using predictive algorithms serves as a new paradigmatic application. In this work, our goal was to explore an ensemble machine learning approach which aims to predict probable antidepressant treatment response and remission in major depressive disorder (MDD). To discover the status of antidepressant treatments, we established an ensemble predictive model with a feature selection algorithm resulting from the analysis of genetic variants and clinical variables of 421 patients who were treated with selective serotonin reuptake inhibitors. We also compared our ensemble machine learning framework with other state-of-the-art models including multi-layer feedforward neural networks (MFNNs), logistic regression, support vector machine, C4.5 decision tree, naïve Bayes, and random forests. Our data revealed that the ensemble predictive algorithm with feature selection (using fewer biomarkers) performed comparably to other predictive algorithms (such as MFNNs and logistic regression) to derive the perplexing relationship between biomarkers and the status of antidepressant treatments. Our study demonstrates that the ensemble machine learning framework may present a useful technique to create bioinformatics tools for discriminating non-responders from responders prior to antidepressant treatments.
Highlights
Nowadays researchers have been making significant progress in the interdisciplinary fields of pharmacogenomics, machine learning, and psychiatry [1,2,3]
Our analysis indicated that the boosting ensemble model with the wrapper-based feature selection algorithm was well-suited for predictive models for antidepressant treatment response
Our analysis found that the boosting ensemble predictive framework with the wrapper-based feature selection algorithm performed comparably to other state-of-the-art predictive models such as logistic regression and the multi-layer feedforward neural networks (MFNNs) model in terms of AUC for distinguishing antidepressant treatment non-responders from responders in major depressive disorder (MDD)
Summary
Nowadays researchers have been making significant progress in the interdisciplinary fields of pharmacogenomics, machine learning, and psychiatry [1,2,3]. In the arena of pharmacogenomics, the goal of machine learning research is to provide predictive algorithms that can in general help facilitate the investigation of how genetic variants and clinical variables can influence an individual’s treatment outcomes to drugs [1,2,3]. Machine learning approaches such as multi-layer feedforward neural networks (MFNNs) have been utilized to infer clinical treatment outcomes in patients with major depressive disorder (MDD) treated with antidepressants by using clinical characteristics and genetic variants such as single nucleotide polymorphisms (SNPs) [4]. Maciukiewicz et al [12] demonstrated a support vector machine (SVM) approach to predict antidepressant treatment response with 52% accuracy in MDD patients by using SNPs. a recent study by Lin et al [4] reported that an MFNN approach can foresee antidepressant treatment response (area under the receiver operating characteristic curve (AUC) = 0.8228) and remission (AUC = 0.8060) by using 10 SNPs and 6 clinical variables
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