ArticleMolecular diversity2010
A knowledge-based method to predict the cooperative relationship between transcription factors.
Article in Molecular diversity, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- A comprehensive performance evaluation on the prediction results of existing cooperative transcription factors identification algorithms.BMC systems biology · 2014Article
- Prediction of interactiveness of proteins and nucleic acids based on feature selections.Molecular diversity · 2010Article
- Prediction of pharmacological and xenobiotic responses to drugs based on time course gene expression profiles.PloS one · 2009Article
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Authors and funding
6 authors.
Funding
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Abstract
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.
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