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However, most proteins function by interacting with other proteins or molecules, and many functional associations should be limited to specific regions rather than the entire protein length. Most domain-centric function prediction methods depend on accurate domain family assignments to infer relationships between domains and functions, with regions that are unassigned to a known domain-family left out of functional evaluation. Given the abundance of residue-level annotations currently available, we present a function prediction methodology that automatically infers function labels of specific protein regions using protein-level annotations and multiple types of region-specific features.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We apply this method to local features obtained from InterPro, UniProtKB and amino acid sequences and show that this method improves both the accuracy and region-specificity of protein function transfer and prediction. We compare region-level predictive performance of our method against that of a whole-protein baseline method using proteins with structurally verified binding sites and also compare protein-level temporal holdout predictive performances to expand the variety and specificity of GO terms we could evaluate. Our results can also serve as a starting point to categorize GO terms into region-specific and whole-protein terms and select prediction methods for different classes of GO terms.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The code and features are freely available at: https:\/\/github.com\/ek1203\/rsfp.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Supplementary information<\/jats:title>\n                    <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/bty834","type":"journal-article","created":{"date-parts":[[2018,10,8]],"date-time":"2018-10-08T23:22:18Z","timestamp":1539040938000},"page":"1737-1744","source":"Crossref","is-referenced-by-count":9,"title":["Towards region-specific propagation of protein functions"],"prefix":"10.1093","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9379-4548","authenticated-orcid":false,"given":"Da Chen Emily","family":"Koo","sequence":"first","affiliation":[{"name":"Department of Biology, Center for Genomics and Systems Biology, New York University, New York, NY, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Richard","family":"Bonneau","sequence":"additional","affiliation":[{"name":"Department of Biology, Center for Genomics and Systems Biology, New York University, New York, NY, USA"},{"name":"Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA"},{"name":"Center for Data Science, New York University, New York, NY, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2018,10,9]]},"reference":[{"key":"2023013107491413600_bty834-B1","volume-title":"Molecular Biology of the Cell","author":"Alberts","year":"2002","edition":"4th edn."},{"key":"2023013107491413600_bty834-B2","doi-asserted-by":"crossref","first-page":"3031","DOI":"10.1021\/acs.jctc.7b00125","article-title":"The Rosetta all-atom energy function for macromolecular modeling and design","volume":"13","author":"Alford","year":"2017","journal-title":"J. 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