Departmental Papers (CIS)

Date of this Version

March 2007

Document Type

Journal Article

Comments

© 2007 Bernal et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Reprinted from PLOS Computational Biology, Volume 3 Issue 3, e54.
Publisher URL: http://dx.doi.org/10.1371/journal.pcbi.0030054

Abstract

Most ab initio gene predictors use a probabilistic sequence model, typically a hidden Markov model, to combine separately trained models of genomic signals and content. By combining separate models of relevant genomic features, such gene predictors can exploit small training sets and incomplete annotations, and can be trained fairly efficiently. However, that type of piecewise training does not optimize prediction accuracy and has difficulty in accounting for statistical dependencies among different parts of the gene model. With genomic information being created at an ever-increasing rate, it is worth investigating alternative approaches in which many different types of genomic evidence, with complex statistical dependencies, can be integrated by discriminative learning to maximize annotation accuracy. Among discriminative learning methods, large-margin classifiers have become prominent because of the success of support vector machines (SVM) in many classification tasks. We describe CRAIG, a new program for ab initio gene prediction based on a conditional random field model with semi-Markov structure that is trained with an online large-margin algorithm related to multiclass SVMs. Our experiments on benchmark vertebrate datasets and on regions from the ENCODE project show significant improvements in prediction accuracy over published gene predictors that use intrinsic features only, particularly at the gene level and on genes with long introns.

Keywords

craig, crf-based ab initio genefinder, crf, conditional random fields, hmm, hidden markov model, mira, margin infused relaxed algorithm, pwm, position weight matrices, svm, support vector machines, tis, translation initiation site, wam, weight array model, wwam, windowed weight array model

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Date Posted: 19 August 2007

This document has been peer reviewed.