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QA methods\u00a0can predict the qualities\u00a0of protein models and\u00a0identify good models from decoys. Clustering-based methods need a certain number of models as input. However, if a pool of models are not available, methods that only need a single model as input are indispensable.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We developed MASS, a QA\u00a0method to predict the\u00a0global qualities of individual protein models using random forests and various novel\u00a0energy functions. We designed six novel energy functions or\u00a0statistical potentials that can capture the structural characteristics of a protein model, which can also\u00a0be used in other protein-related bioinformatics research. MASS potentials demonstrated higher importance\u00a0than the\u00a0energy functions\u00a0of\u00a0RWplus, GOAP, DFIRE\u00a0and Rosetta when the scores\u00a0they generated\u00a0are\u00a0used as machine learning features.\u00a0MASS outperforms almost all of the\u00a0four CASP11 top-performing single-model methods for global quality assessment in terms of all of the four evaluation criteria\u00a0officially used by CASP, which measure the abilities to assign relative and absolute scores, identify the\u00a0best model from decoys, and distinguish between good and bad models. MASS has also achieved comparable performances with the\u00a0leading QA\u00a0methods in CASP12 and CASP13.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>MASS and the source code for all MASS potentials are publicly available at<jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"http:\/\/dna.cs.miami.edu\/MASS\/\">http:\/\/dna.cs.miami.edu\/MASS\/<\/jats:ext-link>.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s12859-020-3383-3","type":"journal-article","created":{"date-parts":[[2020,7,6]],"date-time":"2020-07-06T10:02:58Z","timestamp":1594029778000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["MASS: predict the global qualities of individual protein models using random forests and novel statistical potentials"],"prefix":"10.1186","volume":"21","author":[{"given":"Tong","family":"Liu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3892-6151","authenticated-orcid":false,"given":"Zheng","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,7,6]]},"reference":[{"key":"3383_CR1","doi-asserted-by":"crossref","unstructured":"Kryshtafovych A, Barbato A, Monastyrskyy B, Fidelis K, Schwede T, Tramontano A. 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