DRSN4mCPred: accurately predicting sites of DNA N4-methylcytosine using deep residual shrinkage network for diagnosis and treatment of gastrointestinal cancer in the precision medicine era - Yu, Xia; Ren, Jia; Cui, Yani; Zeng, Rao; Long, Haixia; Ma, Cuihua (Frontiers in Medicine, 2023)
The DNA N4-methylcytosine (4mC) sites levels of those suffering from digestive system cancers were higher, and the pathogenesis of digestive system cancers may also be related to the changes in DNA 4mC levels. Identifying DNA 4mC sites is a very important step in studying the analysis of biological function and cancer prediction. Extracting of accurate features from DNA sequences is the key to establish an prediction model of effective DNA 4mC sites. This study sought to develop a new predictive model, DRSN4mCPred, which aimed to improve the performance of the predicting DNA 4mC sites.The model adopted multi-scale channel attention to extract features, and used attention feature fusion (AFF) to fuse features. In order to capture features information more accurately and effectively, this model utilized Deep Residual Shrinkage Network with Channel-Wise thresholds (DRSN-CW) to eliminate noise-related features and achieve a more precise feature representation, thereby distinguishing the sites in DNA with 4mC and non-4mC. Additionally, the predictive model incorporated an inverted residual block, a Multi-scale Channel Attention Module (MS-CAM), a Bi-directional Long Short Term Memory Network (Bi-LSTM), AFF and DRSN-CW. The results indicated the predictive model DRSN4mCPred had extremely good performance in predicting the DNA 4mC sites across different species compared to the existing excellent predictive model. This paper will potentially provide support for diagnostics and treatment of gastrointestinal cancer based on the artificial intelligence in the precise medical era.