{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T22:40:47Z","timestamp":1761864047077},"reference-count":16,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2019,12,28]],"date-time":"2019-12-28T00:00:00Z","timestamp":1577491200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,1,18]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>A review, recently published in this journal by Fang (2019), showed that methods trained for the prediction of protein stability changes upon mutation have a very critical bias: they neglect that a protein variation (A-\u2009&amp;gt;\u2009B) and its reverse (B-\u2009&amp;gt;\u2009A) must have the opposite value of the free energy difference (\u0394\u0394GAB\u00a0=\u2009\u2212 \u0394\u0394GBA). In this letter, we complement the Fang\u2019s paper presenting a more general view of the problem. In particular, a machine learning-based method, published in 2015 (INPS), addressed the bias issue directly. We include the analysis of the missing method, showing that INPS is nearly insensitive to the addressed problem.<\/jats:p>","DOI":"10.1093\/bib\/bbz168","type":"journal-article","created":{"date-parts":[[2019,12,6]],"date-time":"2019-12-06T12:36:25Z","timestamp":1575635785000},"page":"601-603","source":"Crossref","is-referenced-by-count":14,"title":["On the critical review of five machine learning-based algorithms for predicting protein stability changes upon mutation"],"prefix":"10.1093","volume":"22","author":[{"given":"Castrense","family":"Savojardo","sequence":"first","affiliation":[{"name":"Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy"}]},{"given":"Pier Luigi","family":"Martelli","sequence":"additional","affiliation":[{"name":"Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy"}]},{"given":"Rita","family":"Casadio","sequence":"additional","affiliation":[{"name":"Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy"}]},{"given":"Piero","family":"Fariselli","sequence":"additional","affiliation":[{"name":"Department of Medical Sciences, University of Torino, Torino, Italy"}]}],"member":"286","published-online":{"date-parts":[[2019,12,28]]},"reference":[{"key":"2021012203305214500_ref1","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbz071","article-title":"A critical review of five machine learning-based algorithms for predicting protein stability changes upon mutation","author":"Fang","year":"2019","journal-title":"Brief Bioinform"},{"key":"2021012203305214500_ref2","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1002\/prot.23163","article-title":"Prots: a fragment based protein thermo-stability potential","volume":"80","author":"Li","year":"2012","journal-title":"Proteins"},{"key":"2021012203305214500_ref3","article-title":"PROTS-RF: A robust model for predicting mutation-induced protein stability 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