RECOMB '03 Proceedings of the seventh annual international conference on Research in computational molecular biology
Combining phylogenetic and hidden Markov models in biosequence analysis
RECOMB '03 Proceedings of the seventh annual international conference on Research in computational molecular biology
A Generalized Hidden Markov Model for the Recognition of Human Genes in DNA
Proceedings of the Fourth International Conference on Intelligent Systems for Molecular Biology
Computational identification of protein-coding sequences by comparative analysis
International Journal of Data Mining and Bioinformatics
Markov model variants for appraisal of coding potential in plant DNA
ISBRA'07 Proceedings of the 3rd international conference on Bioinformatics research and applications
Advancing the state of the art in computational gene prediction
KDECB'06 Proceedings of the 1st international conference on Knowledge discovery and emergent complexity in bioinformatics
Donor recognition synthesis method base on simulate anneal
LSMS'07 Proceedings of the Life system modeling and simulation 2007 international conference on Bio-Inspired computational intelligence and applications
An evolutionary algorithm for gene structure prediction
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part II
Using multiple alignments to improve gene prediction
RECOMB'05 Proceedings of the 9th Annual international conference on Research in Computational Molecular Biology
Toward a phylogenetically aware algorithm for fast DNA similarity search
RCG'04 Proceedings of the 2004 RECOMB international conference on Comparative Genomics
New methods for detecting lineage-specific selection
RECOMB'06 Proceedings of the 10th annual international conference on Research in Computational Molecular Biology
Efficient classifiers for multi-class classification problems
Decision Support Systems
Compression of whole genome alignments using a mixture of finite-context models
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part I
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Phylogenetic hidden Markov models (phylo-HMMs) have recently been proposed as a means for addressing a multi-species version of the ab initio gene prediction problem. These models allow sequence divergence, a phylogeny, patterns of substitution, and base composition all to be considered simultaneously, in a single unified probabilistic model. Here, we apply phylo-HMMs to a restricted version of the gene prediction problem in which individual exons are sought that are evolutionarily conserved across a diverse set of species. We discuss two new methods for improving prediction performance: (1) the use of context-dependent phylogenetic models, which capture phenomena such as a strong CpG effect in noncoding regions and a preference for synonymous rather than nonsynonymous substitutions in coding regions; and (2) a novel strategy for incorporating insertions and deletion (indels) into the state-transition structure of the model, which captures the different characteristic patterns of alignment gaps in coding and noncoding regions. We also discuss the technique, previously used in pairwise gene predictors, of explicitly modeling conserved noncoding sequence to help reduce false positive predictions. These methods have been incorporated into an exon prediction program called ExoniPhy, and tested with two large data sets. Experimental results indicate that all three methods produce significant improvements in prediction performance. In combination, they lead to prediction accuracy comparable to that of some of the best available gene predictors, despite several limitations of our current models.