title: Predicting tissue-specific expressions based on sequence characteristics.
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.
authors.affiliation: Plant Systems Engineering Center, KRIBB, Daejeon, Korea.
authors.forename: Hyojung
authors.initials: H
authors.lastname: Paik
authors.forename: Taewoo
authors.initials: T
authors.lastname: Ryu
authors.forename: Hyoung-Sam
authors.initials: HS
authors.lastname: Heo
authors.forename: Seung-Won
authors.initials: SW
authors.lastname: Seo
authors.forename: Doheon
authors.initials: D
authors.lastname: Lee
authors.forename: Cheol-Goo
authors.initials: CG
authors.lastname: Hur
mesh_terms: D000465:Algorithms; D001665:Binding Sites; D030541:Databases, Genetic; D020224:Expressed Sequence Tags; D020869:Gene Expression Profiling; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis; D017422:Sequence Analysis, DNA; D014157:Transcription Factors / Q000235:genetics / Q000378:metabolism*
chemical_list: D014157:Transcription Factors