{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T11:43:08Z","timestamp":1790422988688,"version":"4.1.0"},"reference-count":26,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2021,2,26]],"date-time":"2021-02-26T00:00:00Z","timestamp":1614297600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Medical Research Council and Health Data Research UK","award":["MR\/S004149\/1"],"award-info":[{"award-number":["MR\/S004149\/1"]}]},{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000269","name":"Economic and Social Research Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000269","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000272","name":"National Institute for Health Research","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000272","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Chief Scientist Office of the Scottish Government Health and Social Care Directorates"},{"DOI":"10.13039\/501100010756","name":"Health and Social Care Research and Development Division","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100010756","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001626","name":"Public Health Agency","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001626","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000274","name":"British Heart Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000274","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Industrial Strategy Challenge","award":["MC_PC_18029"],"award-info":[{"award-number":["MC_PC_18029"]}]},{"name":"Wellcome Institutional Translation Partnership Award","award":["PIII054"],"award-info":[{"award-number":["PIII054"]}]},{"DOI":"10.13039\/501100009187","name":"Medical Research Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100009187","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,9,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Network-based gene prioritization algorithms are designed to prioritize disease-associated genes based on known ones using biological networks of protein interactions, gene\u2013disease associations (GDAs) and other relationships between biological entities. Various algorithms have been developed based on different mechanisms, but it is not obvious which algorithm is optimal for a specific disease. To address this issue, we benchmarked multiple algorithms for their application in cerebral small vessel disease (cSVD). We curated protein\u2013gene interactions (PGIs) and GDAs from databases and assembled PGI networks and disease\u2013gene heterogeneous networks. A screening of algorithms resulted in seven representative algorithms to be benchmarked. Performance of algorithms was assessed using both leave-one-out cross-validation (LOOCV) and external validation with MEGASTROKE genome-wide association study (GWAS). We found that random walk with restart on the heterogeneous network (RWRH) showed best LOOCV performance, with median LOOCV rediscovery rank of 185.5 (out of 19\u00a0463 genes). The GenePanda algorithm had most GWAS-confirmable genes in top 200 predictions, while RWRH had best ranks for small vessel stroke-associated genes confirmed in GWAS. In conclusion, RWRH has overall better performance for application in cSVD despite its susceptibility to bias caused by degree centrality. Choice of algorithms should be determined before applying to specific disease. Current pure network-based gene prioritization algorithms are unlikely to find novel disease-associated genes that are not associated with known ones. The tools for implementing and benchmarking algorithms have been made available and can be generalized for other diseases.<\/jats:p>","DOI":"10.1093\/bib\/bbab006","type":"journal-article","created":{"date-parts":[[2021,1,5]],"date-time":"2021-01-05T22:22:16Z","timestamp":1609885336000},"source":"Crossref","is-referenced-by-count":31,"title":["Benchmarking network-based gene prioritization methods for cerebral small vessel disease"],"prefix":"10.1093","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1625-4067","authenticated-orcid":false,"given":"Huayu","family":"Zhang","sequence":"first","affiliation":[{"name":"Centre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amy","family":"Ferguson","sequence":"additional","affiliation":[{"name":"Centre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Grant","family":"Robertson","sequence":"additional","affiliation":[{"name":"Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, Edinburgh, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muchen","family":"Jiang","sequence":"additional","affiliation":[{"name":"Edinburgh Medical School, University of Edinburgh, Edinburgh, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5310-8766","authenticated-orcid":false,"given":"Teng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Orthopaedics and Traumatology, the University of Hong Kong, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cathie","family":"Sudlow","sequence":"additional","affiliation":[{"name":"Centre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom"},{"name":"Health Data Research UK, London, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keith","family":"Smith","sequence":"additional","affiliation":[{"name":"Centre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom"},{"name":"Health Data Research UK, London, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kristiina","family":"Rannikmae","sequence":"additional","affiliation":[{"name":"Centre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom"},{"name":"Health Data Research UK, London, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Honghan","family":"Wu","sequence":"additional","affiliation":[{"name":"Health Data Research UK, London, United Kingdom"},{"name":"Institute of Health Informatics, University College London, London, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2021,2,26]]},"reference":[{"issue":"6224","key":"2021090813443325200_ref1","doi-asserted-by":"crossref","first-page":"1257601","DOI":"10.1126\/science.1257601","article-title":"Disease networks. 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