Optimized mixed Markov models for motif identification
1 Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27606, USA
2 Biostatistics Branch, The National Institute of Environmental Health Sciences, National Institutes of Health, RTP, NC 27709, USA
3 Institute for Genome Sciences & Policy, Duke University Medical Center, Durham, NC 27708, USA
BMC Bioinformatics 2006, 7:279 doi:10.1186/1471-2105-7-279Published: 2 June 2006
Identifying functional elements, such as transcriptional factor binding sites, is a fundamental step in reconstructing gene regulatory networks and remains a challenging issue, largely due to limited availability of training samples.
We introduce a novel and flexible model, the
Our optimized mixture of Markov models represents an alternative to the existing methods for modeling dependent structures within a biological motif. Our model is conceptually simple and effective, and can improve prediction accuracy and/or computational speed over other leading methods.