CONTRAST predicts protein-coding genes from a multiple genomic alignment using a combination of discriminative machine learning techniques. A two-stage approach is used, in which output from local classifiers is combined with a global model of gene structure. CONTRAST is trained using a novel procedure designed to maximize expected coding region boundary detection accuracy.
Chuong Do (firstname.lastname@example.org)
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Gross SS, Do CB, Sirota M, Batzoglou S.
CONTRAST: A Discriminative, Phylogeny-free Approach to Multiple Informant De Novo Gene Prediction.
Genome Biology, submitted.