國家衛生研究院 NHRI:Item 3990099045/13027
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    Please use this identifier to cite or link to this item: http://ir.nhri.org.tw/handle/3990099045/13027


    Title: Prediction of antidepressant treatment response and remission using an ensemble machine learning framework
    Authors: Lin, E;Kuo, PH;Liu, YL;Yu, YWY;Yang, AC;Tsai, SJ
    Contributors: Center for Neuropsychiatric Research
    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.
    Date: 2020-10-13
    Relation: Pharmaceuticals. 2020 Oct 13;13(10):Article number 305.
    Link to: http://dx.doi.org/10.3390/ph13100305
    JIF/Ranking 2023: http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=NHRI&SrcApp=NHRI_IR&KeyISSN=1424-8247&DestApp=IC2JCR
    Cited Times(WOS): https://www.webofscience.com/wos/woscc/full-record/WOS:000586926600001
    Cited Times(Scopus): https://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85092519757
    Appears in Collections:[Yu-Li Liu] Periodical Articles

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