期刊
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
卷 20, 期 1, 页码 371-377出版社
IEEE COMPUTER SOC
DOI: 10.1109/TCBB.2022.3167468
关键词
Deleterious synonymous mutation; feature representation learning; ensemble learning; pathogenicity prediction
With the increasing importance of identifying deleterious synonymous mutations, this study developed a robust and accurate predictor called frDSM for deleterious synonymous mutation prediction using logistic regression. An effective feature representation learning method was introduced, which achieved better prediction results than competing methods with 31 optimal features.
With the discovery of causality between synonymous mutations and diseases, it has become increasingly important to identify deleterious synonymous mutations for better understanding of their functional mechanisms. Although several machine learning methods have been proposed to solve the task, an effective feature representation method that can make use of the inner difference and relevance between deleterious and benign synonymous mutations is still challenging considering the vast number of synonymous mutations in human genome. In thiswork, we developed a robust and accurate predictor called frDSM for deleterious synonymous mutation prediction using logistic regression. More specifically, we introduced an effective feature representation learning method which exploits multiple feature descriptors from different perspectives including functional scores obtained from previously computational methods, evolutionary conservation, splicing and sequence feature descriptors, and these features descriptors were input into the 76 XGBoost classifiers to obtain the predictive probabilities values. These probabilitieswere concatenated to generate the 76-dimension new feature vector, and feature selection method was used to remove redundant and irrelevant features. Experimental results show that frDSM enables robust and accurate prediction than the competing prediction methods with 31 optimal features, which demonstrated the effectiveness of the feature representation learning method. frDSM is freely available at http://frdsm.xialab.info.
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