{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,28]],"date-time":"2025-12-28T07:53:12Z","timestamp":1766908392131},"reference-count":18,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,5,27]],"date-time":"2024-05-27T00:00:00Z","timestamp":1716768000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,5,27]],"date-time":"2024-05-27T00:00:00Z","timestamp":1716768000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"National Research Centre Egypt"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>MPS III is an autosomal recessive lysosomal storage disease caused mainly by missense variants in the\u00a0<jats:italic>NAGLU, GNS, HGSNAT<\/jats:italic>, and <jats:italic>SGSH<\/jats:italic> genes. The\u00a0pathogenicity interpretation of missense variants is still challenging. We aimed to develop unsupervised clustering-based pathogenicity predictor scores using extracted features from eight in silico predictors to predict the impact of novel missense variants of Sanfilippo syndrome. The model was trained on a dataset consisting of 415 uncertain significant (VUS) missense <jats:italic>NAGLU<\/jats:italic> variants. Performance The SanfilippoPred tool was evaluated by validation and test datasets consisting of 197-labelled <jats:italic>NAGLU<\/jats:italic> missense variants, and its performance was compared versus individual pathogenicity predictors using receiver operating characteristic (ROC) analysis. Moreover, we tested the\u00a0SanfilippoPred tool using extra-labelled 427 missense variants to assess its specificity and sensitivity threshold. Application of the trained machine learning (ML) model on the test dataset of labelled <jats:italic>NAGLU<\/jats:italic> missense variants showed that SanfilippoPred has an accuracy of 0.93 (0.86\u20130.97 at CI 95%), sensitivity of 0.93, and specificity of 0.92. The comparative performance of the\u00a0SanfilippoPred showed better performance (AUC\u2009=\u20090.908) than the individual predictors SIFT (AUC\u2009=\u20090.756), Polyphen-2 (AUC\u2009=\u20090.788), CADD (AUC\u2009=\u20090.568), REVEL (AUC\u2009=\u20090.548), MetaLR (AUC\u2009=\u20090.751), and AlphMissense (AUC\u2009=\u20090.885). Using high-confidence labelled <jats:italic>NAGLU<\/jats:italic> variants, showed that SanfilippoPred has an\u00a085.7% sensitivity threshold. The poor correlation between the Sanfilippo syndrome phenotype and genotype represents a demand for a new tool to classify its missense variants. This study provides a\u00a0significant tool for\u00a0preventing the\u00a0misinterpretation of missense variants of the Sanfilippo syndrome-relevant genes. Finally, it seems that ML-based pathogenicity predictors and Sanfilippo syndrome-specific prediction tools could be feasible and\u00a0efficient pathogenicity predictors in the future.<\/jats:p>","DOI":"10.1038\/s41598-024-62352-0","type":"journal-article","created":{"date-parts":[[2024,5,27]],"date-time":"2024-05-27T19:01:43Z","timestamp":1716836503000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Novel gene-specific Bayesian Gaussian mixture model to predict the missense variants pathogenicity of Sanfilippo syndrome"],"prefix":"10.1038","volume":"14","author":[{"given":"Eman E. 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