Learning probabilistic models of cis-regulatory modules that represent logical and spatial aspects.
BACKGROUND
The process of transcription is controlled by systems of factors which bind in specific arrangements, called cis-regulatory modules (CRMs), in promoter regions. We present a discriminative learning algorithm which simultaneously learns the DNA binding site motifs as well as the logical structure and spatial aspects of CRMs.


RESULTS
Our results on yeast datasets show better predictive accuracy than a current state-of-the-art approach on the same datasets. Our results on yeast, fly and human datasets show that the inclusion of logical and spatial aspects improves the predictive accuracy of our learned models.


BACKGROUND
Source code is available at http://www.cs.wisc.edu/~noto/crm
Noto
Keith
K
Department of Computer Sciences and Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI 53706, USA. noto@cs.wisc.edu
Craven
Mark
M
D000465:Algorithms; D001185:Artificial Intelligence; D001483:Base Sequence; D002874:Chromosome Mapping / Q000379:methods*; D003198:Computer Simulation; D016681:Genome, Fungal / Q000235:genetics*; D008957:Models, Genetic; D015233:Models, Statistical; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated / Q000379:methods; D050436:Regulatory Elements, Transcriptional / Q000235:genetics*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016415:Sequence Alignment / Q000379:methods; D017422:Sequence Analysis, DNA / Q000379:methods*; D014157:Transcription Factors / Q000235:genetics*
D014157:Transcription Factors