We describe CONTRAST, a gene predictor which directly incorporates information from multiple alignments rather than employing phylogenetic models. This is accomplished through the use of discriminative machine learning techniques, including a novel training algorithm. We use a two-stage approach, in which a set of binary classifiers designed to recognize coding region boundaries is combined with a global model of gene structure. CONTRAST predicts exact coding region structures for 65% more human genes than the previous state-of-the-art method, misses 46% fewer exons and displays comparable gains in specificity. © 2007 Gross et al.; licensee BioMed Central Ltd.
CITATION STYLE
Gross, S. S., Do, C. B., Sirota, M., & Batzoglou, S. (2007). CONTRAST: A discriminative, phylogeny-free approach to multiple informant de novo gene prediction. Genome Biology, 8(12). https://doi.org/10.1186/gb-2007-8-12-r269
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