{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T05:46:15Z","timestamp":1772516775713,"version":"3.50.1"},"reference-count":62,"publisher":"Oxford University Press (OUP)","issue":"21","license":[{"start":{"date-parts":[[2019,4,8]],"date-time":"2019-04-08T00:00:00Z","timestamp":1554681600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"National Institute of Health","award":["T15LM009451"],"award-info":[{"award-number":["T15LM009451"]}]},{"name":"National Institute of Health","award":["R01AA021131"],"award-info":[{"award-number":["R01AA021131"]}]},{"name":"National Institute of Health","award":["R01HL125583"],"award-info":[{"award-number":["R01HL125583"]}]},{"name":"National Institute of Health","award":["P30DA044223"],"award-info":[{"award-number":["P30DA044223"]}]},{"name":"National Institute of Health","award":["K08HL136928"],"award-info":[{"award-number":["K08HL136928"]}]},{"name":"National Institute of Health","award":["R01HL130512"],"award-info":[{"award-number":["R01HL130512"]}]},{"name":"National Institute of Health","award":["U01HL089897"],"award-info":[{"award-number":["U01HL089897"]}]},{"name":"National Institute of Health","award":["U01HL089856"],"award-info":[{"award-number":["U01HL089856"]}]},{"name":"National Institute of Health","award":["R01HL124233"],"award-info":[{"award-number":["R01HL124233"]}]},{"name":"National Institute of Health","award":["R01HL126596"],"award-info":[{"award-number":["R01HL126596"]}]},{"DOI":"10.13039\/100005834","name":"Francis Family Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100005834","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Parker B. Francis Research Opportunity Award"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,11,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Complex diseases often involve a wide spectrum of phenotypic traits. Better understanding of the biological mechanisms relevant to each trait promotes understanding of the etiology of the disease and the potential for targeted and effective treatment plans. There have been many efforts towards omics data integration and network reconstruction, but limited work has examined the incorporation of relevant (quantitative) phenotypic traits.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We propose a novel technique, sparse multiple canonical correlation network analysis (SmCCNet), for integrating multiple omics data types along with a quantitative phenotype of interest, and for constructing multi-omics networks that are specific to the phenotype. As a case study, we focus on miRNA\u2013mRNA networks. Through simulations, we demonstrate that SmCCNet has better overall prediction performance compared to popular gene expression network construction and integration approaches under realistic settings. Applying SmCCNet to studies on chronic obstructive pulmonary disease (COPD) and breast cancer, we found enrichment of known relevant pathways (e.g. the Cadherin pathway for COPD and the interferon-gamma signaling pathway for breast cancer) as well as less known omics features that may be important to the diseases. Although those applications focus on miRNA\u2013mRNA co-expression networks, SmCCNet is applicable to a variety of omics and other data types. It can also be easily generalized to incorporate multiple quantitative phenotype simultaneously. The versatility of SmCCNet suggests great potential of the approach in many areas.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The SmCCNet algorithm is written in R, and is freely available on the web at https:\/\/cran.r-project.org\/web\/packages\/SmCCNet\/index.html.<\/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\/btz226","type":"journal-article","created":{"date-parts":[[2019,4,5]],"date-time":"2019-04-05T21:11:36Z","timestamp":1554498696000},"page":"4336-4343","source":"Crossref","is-referenced-by-count":34,"title":["Unsupervised discovery of phenotype-specific multi-omics networks"],"prefix":"10.1093","volume":"35","author":[{"given":"W Jenny","family":"Shi","sequence":"first","affiliation":[{"name":"Computational Bioscience Program, University of Colorado Anschutz Medical Campus , Aurora, CO, USA"}]},{"given":"Yonghua","family":"Zhuang","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus , Aurora, CO, USA"}]},{"given":"Pamela H","family":"Russell","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus , Aurora, CO, USA"}]},{"given":"Brian D","family":"Hobbs","sequence":"additional","affiliation":[{"name":"Channing Division of Network Medicine, Brigham and Women\u2019s Hospital , Boston, MA, USA"},{"name":"Division of Pulmonary and Critical Care Medicine, Brigham and Women\u2019s Hospital , Boston, MA, USA"}]},{"given":"Margaret M","family":"Parker","sequence":"additional","affiliation":[{"name":"Channing Division of Network Medicine, Brigham and Women\u2019s Hospital , Boston, MA, USA"}]},{"given":"Peter J","family":"Castaldi","sequence":"additional","affiliation":[{"name":"Channing Division of Network Medicine, Brigham and Women\u2019s Hospital , Boston, MA, USA"}]},{"given":"Pratyaydipta","family":"Rudra","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus , Aurora, CO, USA"},{"name":"Department of Statistics, Oklahoma State University , Stillwater, OK"}]},{"given":"Brian","family":"Vestal","sequence":"additional","affiliation":[{"name":"Center for Genes, Environment & Health, National Jewish Health , Denver, CO, USA"}]},{"given":"Craig P","family":"Hersh","sequence":"additional","affiliation":[{"name":"Channing Division of Network Medicine, Brigham and Women\u2019s Hospital , Boston, MA, USA"},{"name":"Division of Pulmonary and Critical Care Medicine, Brigham and Women\u2019s Hospital , Boston, MA, USA"}]},{"given":"Laura M","family":"Saba","sequence":"additional","affiliation":[{"name":"Department of Pharmaceutical Sciences, University of Colorado , Aurora, CO, USA"}]},{"given":"Katerina","family":"Kechris","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus , Aurora, CO, USA"}]}],"member":"286","published-online":{"date-parts":[[2019,4,8]]},"reference":[{"key":"2023062712515962500_btz226-B1","doi-asserted-by":"crossref","first-page":"e0152032.","DOI":"10.1371\/journal.pone.0152032","article-title":"Tissue and serum miRNA profile in locally advanced breast cancer (LABC) in response to neo-adjuvant chemotherapy (NAC) treatment","volume":"11","author":"Al-Khanbashi","year":"2016","journal-title":"PLoS One"},{"key":"2023062712515962500_btz226-B2","doi-asserted-by":"crossref","first-page":"180.","DOI":"10.1186\/s12859-016-1043-4","article-title":"Integration of multi-omics data for prediction of phenotypic traits using random forest","volume":"17","author":"Acharjee","year":"2016","journal-title":"BMC Bioinform"},{"key":"2023062712515962500_btz226-B3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1371\/journal.pcbi.1003983","article-title":"Integrative multi-omics module network inference with lemon-tree","volume":"11","author":"Bonnet","year":"2015","journal-title":"PLoS Comput. 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