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Optimized mixed Markov models for motif identification

Item Type:Article
Title:Optimized mixed Markov models for motif identification
Creators Name:Huang, W. and Umbach, D.M. and Ohler, U. and Li, L.
Abstract:Background: 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. Results: We introduce a novel and flexible model, the Optimized Mixture Markov model (OMiMa), and related methods to allow adjustment of model complexity for different motifs. In comparison with other leading methods, OMiMa can incorporate more than the NNSplice's pairwise dependencies; OMiMa avoids model over-fitting better than the Permuted Variable Length Markov Model (PVLMM); and OMiMa requires smaller training samples than the Maximum Entropy Model (MEM). Testing on both simulated and actual data (regulatory cis-elements and splice sites), we found OMiMa's performance superior to the other leading methods in terms of prediction accuracy, required size of training data or computational time. Our OMiMa system, to our knowledge, is the only motif finding tool that incorporates automatic selection of the best model. OMiMa is freely available at [1]. Conclusion: 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.
Keywords:Algorithms, Automated Pattern Recognition, Binding Sites, Computational Biology, Computer Simulation, Likelihood Functions, Markov Chains, Programming Languages, Protein Binding, Proteins, Proteomics, Protein Sequence Analysis, ROC Curve, Statistical Models, Software, Theoretical Models
Source:BMC Bioinformatics
ISSN:1471-2105
Publisher:BioMed Central (U.K.)
Volume:7
Page Range:279
Date:2 June 2006
Official Publication:https://doi.org/10.1186/1471-2105-7-279
PubMed:View item in PubMed

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