A knowledge-based method to predict the cooperative relationship between transcription factors.
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.
Lu
Lingyi
L
Key Lab of Molecular Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai, 200031, China.
Qian
Ziliang
Z
Shi
XiaoHe
X
Li
Haipeng
H
Cai
Yu-Dong
YD
Li
Yixue
Y
D000465:Algorithms; D000595:Amino Acid Sequence / Q000502:physiology; D001185:Artificial Intelligence; D001665:Binding Sites / Q000235:genetics; D001667:Binding, Competitive / Q000502:physiology; D019295:Computational Biology / Q000379:methods*; D004357:Drug Synergism; D005544:Forecasting; D051188:Knowledge Bases; D011485:Protein Binding; D013329:Structure-Activity Relationship; D014157:Transcription Factors / Q000737:chemistry* / Q000378:metabolism / Q000494:pharmacology* / Q000502:physiology; D015533:Transcriptional Activation / Q000187:drug effects / Q000502:physiology
D014157:Transcription Factors