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Recent experimental studies have revealed that S-sulphenylation plays critical roles in many biological functions, such as protein regulation and cell signaling. State-of-the-art bioinformatic advances have facilitated high-throughput in silico screening of protein S-sulphenylation sites, thereby significantly reducing the time and labour costs traditionally required for the experimental investigation of S-sulphenylation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In this study, we have proposed a novel hybrid computational framework, termed<jats:italic>SIMLIN<\/jats:italic>, for accurate prediction of protein S-sulphenylation sites using a multi-stage neural-network based ensemble-learning model integrating both protein sequence derived and protein structural features. Benchmarking experiments against the current state-of-the-art predictors for S-sulphenylation demonstrated that<jats:italic>SIMLIN<\/jats:italic>delivered competitive prediction performance. The empirical studies on the independent testing dataset demonstrated that<jats:italic>SIMLIN<\/jats:italic>achieved 88.0% prediction accuracy and an AUC score of 0.82, which outperforms currently existing methods.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>In summary,<jats:italic>SIMLIN<\/jats:italic>predicts human S-sulphenylation sites with high accuracy thereby facilitating biological hypothesis generation and experimental validation. The web server, datasets, and online instructions are freely available at<jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"http:\/\/simlin.erc.monash.edu\">http:\/\/simlin.erc.monash.edu<\/jats:ext-link>\/ for academic purposes.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s12859-019-3178-6","type":"journal-article","created":{"date-parts":[[2019,11,21]],"date-time":"2019-11-21T12:04:07Z","timestamp":1574337847000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["SIMLIN: a bioinformatics tool for prediction of S-sulphenylation in the\u00a0human proteome based on multi-stage ensemble-learning models"],"prefix":"10.1186","volume":"20","author":[{"given":"Xiaochuan","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fuyi","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Varun S.","family":"Sharma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8031-9086","authenticated-orcid":false,"given":"Jiangning","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Geoffrey I.","family":"Webb","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,11,21]]},"reference":[{"issue":"4-5","key":"3178_CR1","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1002\/pmic.201300344","volume":"14","author":"A. 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