Predicting tissue-specific expressions based on sequence characteristics

Hyojung Paik, Tae Woo Ryu, Hyoungsam Heo, Seungwon Seo, Doheon Lee, Cheolgoo Hur

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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.
Original languageEnglish (US)
Pages (from-to)250-255
Number of pages6
JournalBMB Reports
Issue number4
StatePublished - Apr 30 2011

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology


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