Prediction of Antidepressant Treatment Response and Remission Using an Ensemble Machine Learning Framework
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 respons...
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doaj-56f72dcf98744112bf8c61bac5daefef2020-11-25T03:38:32ZengMDPI AGPharmaceuticals1424-82472020-10-011330530510.3390/ph13100305Prediction of Antidepressant Treatment Response and Remission Using an Ensemble Machine Learning FrameworkEugene Lin0Po-Hsiu Kuo1Yu-Li Liu2Younger W.-Y. Yu3Albert C. Yang4Shih-Jen Tsai5Department of Biostatistics, University of Washington, Seattle, WA 98195, USADepartment of Public Health, Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei 10617, TaiwanCenter for Neuropsychiatric Research, National Health Research Institutes, Miaoli County 35053, TaiwanYu’s Psychiatric Clinic, Kaohsiung 802211, TaiwanDivision of Interdisciplinary Medicine and Biotechnology, Beth Israel Deaconess Medical Center/Harvard Medical School, Boston, MA 02215, USAInstitute of Brain Science, National Yang-Ming University, Taipei 112304, TaiwanIn 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.https://www.mdpi.com/1424-8247/13/10/305antidepressantensemble learningfeature selectionmachine learningmajor depressive disorderpharmacogenomics |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Eugene Lin Po-Hsiu Kuo Yu-Li Liu Younger W.-Y. Yu Albert C. Yang Shih-Jen Tsai |
spellingShingle |
Eugene Lin Po-Hsiu Kuo Yu-Li Liu Younger W.-Y. Yu Albert C. Yang Shih-Jen Tsai Prediction of Antidepressant Treatment Response and Remission Using an Ensemble Machine Learning Framework Pharmaceuticals antidepressant ensemble learning feature selection machine learning major depressive disorder pharmacogenomics |
author_facet |
Eugene Lin Po-Hsiu Kuo Yu-Li Liu Younger W.-Y. Yu Albert C. Yang Shih-Jen Tsai |
author_sort |
Eugene Lin |
title |
Prediction of Antidepressant Treatment Response and Remission Using an Ensemble Machine Learning Framework |
title_short |
Prediction of Antidepressant Treatment Response and Remission Using an Ensemble Machine Learning Framework |
title_full |
Prediction of Antidepressant Treatment Response and Remission Using an Ensemble Machine Learning Framework |
title_fullStr |
Prediction of Antidepressant Treatment Response and Remission Using an Ensemble Machine Learning Framework |
title_full_unstemmed |
Prediction of Antidepressant Treatment Response and Remission Using an Ensemble Machine Learning Framework |
title_sort |
prediction of antidepressant treatment response and remission using an ensemble machine learning framework |
publisher |
MDPI AG |
series |
Pharmaceuticals |
issn |
1424-8247 |
publishDate |
2020-10-01 |
description |
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. |
topic |
antidepressant ensemble learning feature selection machine learning major depressive disorder pharmacogenomics |
url |
https://www.mdpi.com/1424-8247/13/10/305 |
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