Linked Life Data

19590970

Source:http://linkedlifedata.com/resource/pubmed/id/19590970

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
4
pubmed:dateCreated
2010-11-25
pubmed:abstractText
Identifying the cooperation between transcription factors is crucial and challenging to uncover the mystery behind the complex gene expression patterns. Computational methods aimed to infer transcription factor cooperation are expected to get good results if we can integrate the knowledge (existed functional/structural annotations) of proteins. In this contribution, we proposed an information integrative computational framework to infer the cooperation between transcription factors, which relies on the hybridization-space method that can integrate the annotation information of proteins. In our computational experiments, by using function domain annotations only, on our testing dataset, the overall prediction accuracy and the specificity reaches 84.3% and 76.9%, respectively, which is a fairly good result and outperforms the prediction by both amino acid composition-based method and BLAST-based approach. The corresponding online service TFIPS (Transcription Factor Interaction Prediction System) is available on http://pcal.biosino.org/cgi-bin/TFIPS/TFIPS.pl.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Nov
pubmed:issn
1573-501X
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
14
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
815-9
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
A knowledge-based method to predict the cooperative relationship between transcription factors.
pubmed:affiliation
Key Lab of Molecular Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai, 200031, China.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't, Validation Studies