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dc.contributor.authorSun, Shuying
dc.contributor.authorBertelsmann, Karina
dc.contributor.authorYu, Linda
dc.contributor.authorSun, Shuying
dc.contributor.authorTian, Sunny
dc.date.accessioned2017-04-13T18:23:20Z
dc.date.available2017-04-13T18:23:20Z
dc.date.issued2016-09
dc.date.submitted2016-08
dc.identifier.issn1176-9351
dc.identifier.urihttp://hdl.handle.net/1721.1/108129
dc.description.abstractHeterogeneous DNA methylation patterns are linked to tumor growth. In order to study DNA methylation heterogeneity patterns for breast cancer cell lines, we comparatively study four metrics: variance, I² statistic, entropy, and methylation state. Using the categorical metric methylation state, we select the two most heterogeneous states to identify genes that directly affect tumor suppressor genes and high- or moderate-risk breast cancer genes. Utilizing the Gene Set Enrichment Analysis software and the ConsensusPath Database visualization tool, we generate integrated gene networks to study biological relations of heterogeneous genes. This analysis has allowed us to contribute 19 potential breast cancer biomarker genes to cancer databases by locating “hub genes” – heterogeneous genes of significant biological interactions, selected from numerous cancer modules. We have discovered a considerable relationship between these hub genes and heterogeneously methylated oncogenes. Our results have many implications for further heterogeneity analyses of methylation patterns and early detection of breast cancer susceptibility.en_US
dc.language.isoen_US
dc.publisherLibertas Academica, Ltd.en_US
dc.relation.isversionofhttp://dx.doi.org/10.4137/cin.s40300en_US
dc.rightsCreative Commons Attribution-NonCommercial 3.0 Unporteden_US
dc.rights.urihttps://creativecommons.org/licenses/by-nc/3.0/en_US
dc.sourceLibertas Academicaen_US
dc.titleDNA Methylation Heterogeneity Patterns in Breast Cancer Cell Linesen_US
dc.typeArticleen_US
dc.identifier.citationSun, Shuying, Sunny Tian, Karina Bertelsmann, Linda Yu, and Shuying Sun. “DNA Methylation Heterogeneity Patterns in Breast Cancer Cell Lines.” Cancer Informatics (September 2016): 1. © 2016 the authors, publisher and licensee Libertas Academica Limiteden_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.mitauthorTian, Sunny
dc.relation.journalCancer Informaticsen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsSun, Shuying; Tian, Sunny; Bertelsmann, Karina; Yu, Linda; Sun, Shuyingen_US
dspace.embargo.termsNen_US
mit.licensePUBLISHER_CCen_US


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