Intelligent extraction versus advanced query: Recognize transcription factors from databases

Zhuo Zhang*, Merlin Veronika, See Kiong Ng, Vladimir B. Bajic

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Many entries in major biological databases have incomplete functional annotation and thus, frequently, it is difficult to identify entries for a specific functional category. We combined information of protein functional domains and gene ontology descriptions for highly accurate identification of transcription factor (TF) entries in Swiss-Prot and Entrez Gene databases. Our method utilizes support vector machines and it efficiently separates TF entries from non-TF entries. The 10-fold cross validation of predictions produced on average a positive predictive value of 97.5% and sensitivity of 93.4%. Using this method we have scanned the whole Swiss-Prot and Entrez Gene databases and extracted 13826 unique TF entries. Based on a separate manual test of 500 randomly chosen extracted TF entries, we found that the non-TF (erroneous) entries were present in 2% of the cases.

Original languageEnglish (US)
Title of host publicationPattern Recognition in Bioinformatics - International Workshop, PRIB 2006, Proceedings
PublisherSpringer Verlag
Pages133-139
Number of pages7
Volume4146 LNBI
ISBN (Print)3540374469, 9783540374466
StatePublished - 2006
Externally publishedYes
EventInternational Workshop on Pattern Recognition in Bioinformatics, PRIB 2006 - Hong Kong, China
Duration: Aug 20 2006Aug 20 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4146 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

OtherInternational Workshop on Pattern Recognition in Bioinformatics, PRIB 2006
CountryChina
CityHong Kong
Period08/20/0608/20/06

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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