Publications by authors named "Bangyi Zhang"
mRNA subcellular localization is a prevalent and essential mechanism that precisely regulates protein translation and significantly impacts various cellular processes. mRNA subcellular localization has advanced the understanding of mRNA function, yet existing methods face limitations, including imbalanced data, suboptimal model performance, and inadequate generalization, particularly in multi-label localization scenarios where solutions are scarce. This study introduces MBSCLoc, a predictor for mRNA multi-label subcellular localization.
View Article and Find Full Text PDFSubcellular localization of messenger ribonucleic acid (mRNA) is a universal mechanism for precise and efficient control of the translation process. Although many computational methods have been constructed by researchers for predicting mRNA subcellular localization, very few of these computational methods have been designed to predict subcellular localization with multiple localization annotations, and their generalization performance could be improved. In this study, the prediction model MSlocPRED was constructed to identify multi-label mRNA subcellular localization.
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- - Glycation is a modification that affects lysine and arginine residues in proteins, which can lead to diseases like Alzheimer's, diabetes, and atherosclerosis; machine learning is now being used to predict glycation sites more efficiently than traditional lab methods.
- - The research introduces Glypred, a model that utilizes ClusterCentroids Undersampling (CCU), LightGBM, and bidirectional LSTM techniques, with a multihead attention mechanism, to enhance the prediction of lysine glycation sites, balancing accuracy and robustness.
- - The study focuses on selecting diverse feature types and implementing a cluster-based undersampling strategy to overcome data imbalance, incorporating multiple feature encoding methods (AAC, KMER, DR, PWAA, and