Predicting tissue-specific expressions based on sequence characteristics

Handle URI:
http://hdl.handle.net/10754/561760
Title:
Predicting tissue-specific expressions based on sequence characteristics
Authors:
Paik, Hyojung; Ryu, Tae Woo; Heo, Hyoungsam; Seo, Seungwon; Lee, Doheon; Hur, Cheolgoo
Abstract:
In multicellular organisms, including humans, understanding expression specificity at the tissue level is essential for interpreting protein function, such as tissue differentiation. We developed a prediction approach via generated sequence features from overrepresented patterns in housekeeping (HK) and tissue-specific (TS) genes to classify TS expression in humans. Using TS domains and transcriptional factor binding sites (TFBSs), sequence characteristics were used as indices of expressed tissues in a Random Forest algorithm by scoring exclusive patterns considering the biological intuition; TFBSs regulate gene expression, and the domains reflect the functional specificity of a TS gene. Our proposed approach displayed better performance than previous attempts and was validated using computational and experimental methods.
KAUST Department:
Biological and Environmental Sciences and Engineering (BESE) Division; Computational Bioscience Research Center (CBRC); Red Sea Research Center (RSRC)
Publisher:
Korean Society for Biochemistry and Molecular Biology
Journal:
BMB Reports
Issue Date:
30-Apr-2011
DOI:
10.5483/BMBRep.2011.44.4.250
PubMed ID:
21524350
Type:
Article
ISSN:
19766696
Appears in Collections:
Articles; Red Sea Research Center (RSRC); Computational Bioscience Research Center (CBRC); Biological and Environmental Sciences and Engineering (BESE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorPaik, Hyojungen
dc.contributor.authorRyu, Tae Wooen
dc.contributor.authorHeo, Hyoungsamen
dc.contributor.authorSeo, Seungwonen
dc.contributor.authorLee, Doheonen
dc.contributor.authorHur, Cheolgooen
dc.date.accessioned2015-08-03T09:03:59Zen
dc.date.available2015-08-03T09:03:59Zen
dc.date.issued2011-04-30en
dc.identifier.issn19766696en
dc.identifier.pmid21524350en
dc.identifier.doi10.5483/BMBRep.2011.44.4.250en
dc.identifier.urihttp://hdl.handle.net/10754/561760en
dc.description.abstractIn multicellular organisms, including humans, understanding expression specificity at the tissue level is essential for interpreting protein function, such as tissue differentiation. We developed a prediction approach via generated sequence features from overrepresented patterns in housekeeping (HK) and tissue-specific (TS) genes to classify TS expression in humans. Using TS domains and transcriptional factor binding sites (TFBSs), sequence characteristics were used as indices of expressed tissues in a Random Forest algorithm by scoring exclusive patterns considering the biological intuition; TFBSs regulate gene expression, and the domains reflect the functional specificity of a TS gene. Our proposed approach displayed better performance than previous attempts and was validated using computational and experimental methods.en
dc.publisherKorean Society for Biochemistry and Molecular Biologyen
dc.subjectDomainen
dc.subjectHousekeepingen
dc.subjectRandom foresten
dc.subjectTissue-specificen
dc.subjectTranscription factor binding siteen
dc.titlePredicting tissue-specific expressions based on sequence characteristicsen
dc.typeArticleen
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Divisionen
dc.contributor.departmentComputational Bioscience Research Center (CBRC)en
dc.contributor.departmentRed Sea Research Center (RSRC)en
dc.identifier.journalBMB Reportsen
dc.contributor.institutionPlant Systems Engineering Center, KRIBB, Daejeon, South Koreaen
dc.contributor.institutionDepartment of Bio and Brain Engineering, KAIST, Daejeon, South Koreaen
dc.contributor.institutionUniversity of Science and Technology (UST), Daejeon, South Koreaen
kaust.authorRyu, Tae Wooen
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