{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T03:59:41Z","timestamp":1778558381549,"version":"3.51.4"},"reference-count":39,"publisher":"Oxford University Press (OUP)","issue":"19","license":[{"start":{"date-parts":[[2019,2,19]],"date-time":"2019-02-19T00:00:00Z","timestamp":1550534400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["1R01LM013061-01"],"award-info":[{"award-number":["1R01LM013061-01"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Recent technology developments have made it possible to generate various kinds of omics data, which provides opportunities to better solve problems such as disease subtyping or disease mapping using more comprehensive omics data jointly. Among many developed data-integration methods, the similarity network fusion (SNF) method has shown a great potential to identify new disease subtypes through separating similar subjects using multi-omics data. SNF effectively fuses similarity networks with pairwise patient similarity measures from different types of omics data into one fused network using both shared and complementary information across multiple types of omics data.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In this article, we proposed an association-signal-annotation boosted similarity network fusion (ab-SNF) method, adding feature-level association signal annotations as weights aiming to up-weight signal features and down-weight noise features when constructing subject similarity networks to boost the performance in disease subtyping. In various simulation studies, the proposed ab-SNF outperforms the original SNF approach without weights. Most importantly, the improvement in the subtyping performance due to association-signal-annotation weights is amplified in the integration process. Applications to somatic mutation data, DNA methylation data and gene expression data of three cancer types from The Cancer Genome Atlas project suggest that the proposed ab-SNF method consistently identifies new subtypes in each cancer that more accurately predict patient survival and are more biologically meaningful.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The R package abSNF is freely available for downloading from https:\/\/github.com\/pfruan\/abSNF.<\/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\/btz124","type":"journal-article","created":{"date-parts":[[2019,2,18]],"date-time":"2019-02-18T04:25:53Z","timestamp":1550463953000},"page":"3718-3726","source":"Crossref","is-referenced-by-count":29,"title":["Using association signal annotations to boost similarity network fusion"],"prefix":"10.1093","volume":"35","author":[{"given":"Peifeng","family":"Ruan","sequence":"first","affiliation":[{"name":"Department of Statistics, Columbian College of Arts and Sciences, The George Washington University , Washington, DC, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ya","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Mailman School of Public Health, Columbia University , New York, NY, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ronglai","family":"Shen","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center , New York, NY, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuang","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Mailman School of Public Health, Columbia University , New York, NY, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2019,2,19]]},"reference":[{"key":"2023013108152394800_btz124-B1","doi-asserted-by":"crossref","first-page":"1327","DOI":"10.1016\/j.cell.2017.05.046","article-title":"Comprehensive and integrative genomic characterization of hepatocellular carcinoma","volume":"169","author":"Ally","year":"2017","journal-title":"Cell"},{"key":"2023013108152394800_btz124-B2","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1145\/279943.279962","article-title":"Combining labeled and unlabeled data with co-training","volume-title":"Proceedings of the Eleventh Annual Conference on Computational Learning Theory","author":"Blum","year":"1998"},{"key":"2023013108152394800_btz124-B3","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1038\/nature11412","article-title":"Comprehensive molecular portraits of human breast tumours","volume":"490","year":"2012","journal-title":"Nature"},{"key":"2023013108152394800_btz124-B4","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1056\/NEJMoa1505917","article-title":"Comprehensive molecular characterization of papillary renal-cell carcinoma","volume":"374","year":"2016","journal-title":"N. 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