{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T00:23:55Z","timestamp":1774052635264,"version":"3.50.1"},"reference-count":37,"publisher":"Oxford University Press (OUP)","issue":"12","license":[{"start":{"date-parts":[[2017,2,14]],"date-time":"2017-02-14T00:00:00Z","timestamp":1487030400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/about_us\/legal\/notices"}],"funder":[{"DOI":"10.13039\/100000001","name":"NSF CAREER","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"NSF CAREER","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"name":"DARPA SIMPLEX"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,6,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: Diffusion-based network models are widely used for protein function prediction using protein network data and have been shown to outperform neighborhood-based and module-based methods. Recent studies have shown that integrating the hierarchical structure of the Gene Ontology (GO) data dramatically improves prediction accuracy. However, previous methods usually either used the GO hierarchy to refine the prediction results of multiple classifiers, or flattened the hierarchy into a function\u2013function similarity kernel. No study has taken the GO hierarchy into account together with the protein network as a two-layer network model.<\/jats:p>\n               <jats:p>Results: We first construct a Bi-relational graph (Birg) model comprised of both protein\u2013protein association and function\u2013function hierarchical networks. We then propose two diffusion-based methods, BirgRank and AptRank, both of which use PageRank to diffuse information on this two-layer graph model. BirgRank is a direct application of traditional PageRank with fixed decay parameters. In contrast, AptRank utilizes an adaptive diffusion mechanism to improve the performance of BirgRank. We evaluate the ability of both methods to predict protein function on yeast, fly and human protein datasets, and compare with four previous methods: GeneMANIA, TMC, ProteinRank and clusDCA. We design four different validation strategies: missing function prediction, de novo function prediction, guided function prediction and newly discovered function prediction to comprehensively evaluate predictability of all six methods. We find that both BirgRank and AptRank outperform the previous methods, especially in missing function prediction when using only 10% of the data for training.<\/jats:p>\n               <jats:p>Availability and Implementation: The MATLAB code is available at https:\/\/github.rcac.purdue.edu\/mgribsko\/aptrank.<\/jats:p>\n               <jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btx029","type":"journal-article","created":{"date-parts":[[2017,2,15]],"date-time":"2017-02-15T08:58:08Z","timestamp":1487149088000},"page":"1829-1836","source":"Crossref","is-referenced-by-count":50,"title":["AptRank: an adaptive PageRank model for protein function prediction on \u00a0 bi-relational graphs"],"prefix":"10.1093","volume":"33","author":[{"given":"Biaobin","family":"Jiang","sequence":"first","affiliation":[{"name":"Department of Biological Sciences, Purdue University, West Lafayette, IN, USA"}]},{"given":"Kyle","family":"Kloster","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Purdue University, West Lafayette, IN, USA"}]},{"given":"David F","family":"Gleich","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Purdue University, West Lafayette, IN, USA"}]},{"given":"Michael","family":"Gribskov","sequence":"additional","affiliation":[{"name":"Department of Biological Sciences, Purdue University, West Lafayette, IN, USA"},{"name":"Department of Computer Science, Purdue University, West Lafayette, IN, USA"}]}],"member":"286","published-online":{"date-parts":[[2017,2,14]]},"reference":[{"key":"2023020301105006900_btx029-B1","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1145\/1148170.1148225","volume-title":"Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Baeza-Yates","year":"2006"},{"key":"2023020301105006900_btx029-B2","doi-asserted-by":"crossref","first-page":"830","DOI":"10.1093\/bioinformatics\/btk048","article-title":"Hierarchical multi-label prediction of gene function","volume":"22","author":"Barutcuoglu","year":"2006","journal-title":"Bioinformatics"},{"key":"2023020301105006900_btx029-B3","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1007\/978-3-319-16706-0_9","volume-title":"Research in Computational Molecular Biology","author":"Cho","year":"2015"},{"key":"2023020301105006900_btx029-B4","doi-asserted-by":"crossref","first-page":"19735","DOI":"10.1073\/pnas.0708838104","article-title":"The heat kernel as the PageRank of a graph","volume":"104","author":"Chung","year":"2007","journal-title":"Proc. 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