Effect Predictor of Driver Synonymous Mutations Based on Multi-Feature Fusion and Iterative Feature Representation Learning

作者：Na Cheng, Chuanmei Bi, Yong Shi, Mengya Liu, A. Cao, Mengkun Ren, Junfeng Xia, Zhen Liang · 发表于：IEEE Journal of Biomedical and Health Informatics · 年份：2023 · DOI：10.1109/jbhi.2023.3343075 · 被引用次数：8 · 研究领域：Machine Learning in Bioinformatics、RNA and protein synthesis mechanisms、Genomics and Phylogenetic Studies

Accurate identification of driver mutations is crucial in genetic studies of human cancers. While numerous cancer driver missense mutations have been identified, research into potential cancer drivers for synonymous mutations has shown limited success to date. Here, we developed a novel machine learning framework, epSMic, for predicting cancer driver synonymous mutations. epSMic employs an iterative feature representation scheme that facilitates the learning of discriminative features from various sequential models in a supervised iterative mode. We constructed the benchmark datasets and encoded the embedding sequence, physicochemical property, and basic information such as conservation and splicing feature. The evaluation results on benchmark test datasets demonstrate that epSMic outperforms existing methods, making it a valuable tool for researchers in identifying functional synonymous mutations in cancer. We hope epSMic can enable researchers to concentrate on synonymous mutations that have a functional impact on cancer.