{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T07:14:22Z","timestamp":1782976462634,"version":"3.54.5"},"reference-count":40,"publisher":"Oxford University Press (OUP)","issue":"18","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2006,9,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: Over 50% of human genes contain CpG islands in their 5\u2032-regions. Methylation patterns of CpG islands are involved in tissue-specific gene expression and regulation. Mis-epigenetic silencing associated with aberrant CpG island methylation is one mechanism leading to the loss of tumor suppressor functions in cancer cells. Large-scale experimental detection of DNA methylation is still both labor-intensive and time-consuming. Therefore, it is necessary to develop in silico approaches for predicting methylation status of CpG islands.<\/jats:p>\n               <jats:p>Results: Based on a recent genome-scale dataset of DNA methylation in human brain tissues, we developed a classifier called MethCGI for predicting methylation status of CpG islands using a support vector machine (SVM). Nucleotide sequence contents as well as transcription factor binding sites (TFBSs) are used as features for the classification. The method achieves specificity of 84.65% and sensitivity of 84.32% on the brain data, and can also correctly predict about two-third of the data from other tissues reported in the MethDB database.<\/jats:p>\n               <jats:p>Availability: An online predictor based on MethCGI is available at<\/jats:p>\n               <jats:p>Contact: \u00a0mzhang@cshl.edu<\/jats:p>\n               <jats:p>Supplementary Information: Supplementary data available at Bioinformatics online and<\/jats:p>","DOI":"10.1093\/bioinformatics\/btl377","type":"journal-article","created":{"date-parts":[[2006,7,13]],"date-time":"2006-07-13T00:39:14Z","timestamp":1152751154000},"page":"2204-2209","source":"Crossref","is-referenced-by-count":95,"title":["Predicting methylation status of CpG islands in the human brain"],"prefix":"10.1093","volume":"22","author":[{"given":"Fang","family":"Fang","sequence":"first","affiliation":[{"name":"Bioinformatics Division, TNLIST, Department of Automation, Tsinghua University 1 \u00a0 1 \u00a0 \u00a0 100084 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shicai","family":"Fan","sequence":"additional","affiliation":[{"name":"Bioinformatics Division, TNLIST, Department of Automation, Tsinghua University 1 \u00a0 1 \u00a0 \u00a0 100084 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuegong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Bioinformatics Division, TNLIST, Department of Automation, Tsinghua University 1 \u00a0 1 \u00a0 \u00a0 100084 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael Q.","family":"Zhang","sequence":"additional","affiliation":[{"name":"Cold Spring Harbor Laboratory, Cold Spring Harbor 2 \u00a0 2 \u00a0 \u00a0 NY 11274, USA"},{"name":"Bioinformatics Division, TNLIST, Department of Automation, Tsinghua University 1 \u00a0 1 \u00a0 \u00a0 100084 China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2006,7,12]]},"reference":[{"key":"2023012409210601500_b1","doi-asserted-by":"crossref","first-page":"11995","DOI":"10.1073\/pnas.90.24.11995","article-title":"Number of CpG islands and genes in human and mouse","volume":"90","author":"Antequera","year":"1993","journal-title":"Proc. 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