期刊
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
卷 26, 期 5, 页码 2379-2387出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JBHI.2021.3123503
关键词
Feature extraction; Logic gates; Bit error rate; Training; Bioinformatics; Proteins; Predictive models; Deep learning; machine learning; post-translational modification (PTM); s-nitrosylation (SNO)
类别
资金
- National Natural Science Foundation of China [62001090]
- Natural Science Foundation of Heilongjiang Province [LH2020F009]
SNO is crucial for plant immune response and human disease treatment, with the efficient prediction tool Mul-SNO showing promising results.
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.
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