{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T00:45:40Z","timestamp":1740185140344,"version":"3.37.3"},"reference-count":33,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2016,9,11]],"date-time":"2016-09-11T00:00:00Z","timestamp":1473552000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/about_us\/legal\/notices"}],"funder":[{"DOI":"10.13039\/100000009","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["CA149653","CA149147","CA184902","CA164384"],"award-info":[{"award-number":["CA149653","CA149147","CA184902","CA164384"]}],"id":[{"id":"10.13039\/100000009","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,1,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>The advent of high-throughput DNA methylation profiling techniques has enabled the possibility of accurate identification of differentially methylated genes for cancer research. The large number of measured loci facilitates whole genome methylation study, yet posing great challenges for differential methylation detection due to the high variability in tumor samples.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We have developed a novel probabilistic approach, differential methylation detection using a hierarchical Bayesian model exploiting local dependency (DM-BLD), to detect differentially methylated genes based on a Bayesian framework. The DM-BLD approach features a joint model to capture both the local dependency of measured loci and the dependency of methylation change in samples. Specifically, the local dependency is modeled by Leroux conditional autoregressive structure; the dependency of methylation changes is modeled by a discrete Markov random field. A hierarchical Bayesian model is developed to fully take into account the local dependency for differential analysis, in which differential states are embedded as hidden variables. Simulation studies demonstrate that DM-BLD outperforms existing methods for differential methylation detection, particularly when the methylation change is moderate and the variability of methylation in samples is high. DM-BLD has been applied to breast cancer data to identify important methylated genes (such as polycomb target genes and genes involved in transcription factor activity) associated with breast cancer recurrence.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and Implementation<\/jats:title>\n                  <jats:p>A Matlab package of DM-BLD is available at http:\/\/www.cbil.ece.vt.edu\/software.htm.<\/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\/btw596","type":"journal-article","created":{"date-parts":[[2016,9,12]],"date-time":"2016-09-12T00:29:01Z","timestamp":1473640141000},"page":"161-168","source":"Crossref","is-referenced-by-count":6,"title":["DM-BLD: differential methylation detection using a hierarchical Bayesian model exploiting local dependency"],"prefix":"10.1093","volume":"33","author":[{"given":"Xiao","family":"Wang","sequence":"first","affiliation":[{"name":"Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, 900 North Glebe Road, Arlington, VA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinghua","family":"Gu","sequence":"additional","affiliation":[{"name":"Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, 900 North Glebe Road, Arlington, VA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Leena","family":"Hilakivi-Clarke","sequence":"additional","affiliation":[{"name":"Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, 3970 Reservoir Road, Washington, DC, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Robert","family":"Clarke","sequence":"additional","affiliation":[{"name":"Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, 3970 Reservoir Road, Washington, DC, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianhua","family":"Xuan","sequence":"additional","affiliation":[{"name":"Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, 900 North Glebe Road, Arlington, VA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2016,9,11]]},"reference":[{"key":"2023020204311686900_btw596-B1","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":"2023020204311686900_btw596-B2","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1093\/biomet\/67.2.261","article-title":"Logistic-normal distributions: some properties and uses","volume":"67","author":"Atchison","year":"1980","journal-title":"Biometrika"},{"key":"2023020204311686900_btw596-B202","doi-asserted-by":"crossref","first-page":"726","DOI":"10.1038\/nrc3130","article-title":"A decade of exploring the cancer epigenome - biological and translational implications","volume":"11","author":"Baylin","year":"2011","journal-title":"Nat. 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