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• W4229452760 abstract "Protein s-nitrosylation (SNO) is one of the most important post-translational modifications and is formed by the covalent modification of nitric oxide and cysteine residues. Extensive studies have shown that SNO plays a pivotal role in the plant immune response and treating various major human diseases. In recent years, SNO sites have become a hot research topic. Traditional biochemical methods for SNO site identification are time-consuming and costly. In this study, we developed an economical and efficient SNO site prediction tool named Mul-SNO. Mul-SNO ensembled current popular and powerful deep learning model bidirectional long short-term memory (BiLSTM) and bidirectional encoder representations from Transformers (BERT). Compared with existing state-of-the-art methods, Mul-SNO obtained better ACC of 0.911 and 0.796 based on 10-fold cross-validation and independent data sets, respectively." @default.
• W4229452760 created "2022-05-11" @default.
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• W4229452760 date "2022-05-01" @default.
• W4229452760 modified "2023-10-18" @default.
• W4229452760 title "Mul-SNO: A Novel Prediction Tool for S-Nitrosylation Sites Based on Deep Learning Methods" @default.
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• W4229452760 doi "https://doi.org/10.1109/jbhi.2021.3123503" @default.
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