| Summary: | 碩士 === 國立清華大學 === 資訊系統與應用研究所 === 104 === Depression is a life-threatening mental health disorder which is expected to be the second leading cause of psychosocial disability throughout the world by 2020 and will become the largest contributor to lost work productivity by 2030 as reported by World Health Organization (WHO, 2012). Despite the availability of various therapeutic options, the underlying pathological mechanisms remain unclear. The important concerns with
antidepressants are delayed therapeutic response and insufficient efficacy. With a wide range of adverse effects, there is no doubt a large unmet need for better pharmaceutical treatment. The purpose of our study is to develop a computational approach to investigate potential side effects and targets of antidepressants, hoping to provide support for better strategies for the future of drug development and therapy.
We presented an aggregation framework to predict unknown side effects and hidden targets from 816 drugs by adopting 653 chemical, 984 biological and 6,111 phenotypic features. Among four machine learning-based algorithms, we found that the aggregation random forest model achieved best in overall performance. Hence, we used this computational approach to predict the potential candidates for antidepressants. We conducted the case
study using 15 depression-related drugs, including 9 first generation, 5 second generation antidepressants and 1 muscle relaxant that has a structure similar to tricyclic antidepressant (TCA). The in silico model obtained promising results with AUROC score of 0.9140834,
AUPR score of 0.5185952 for side effects prediction and AUROC score of 0.9513566, AUPR score of 0.3101223 for targets prediction.
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