global discriminative learning for higher-accuracy computational gene prediction全球有识别力的学习精度高计算基因预测.pdfVIP

Global Discriminative Learning for Higher- Accuracy Computational Gene Prediction 1* 1 2 1 Axel Bernal , Koby Crammer , Artemis Hatzigeorgiou , Fernando Pereira 1 Department of Computer and Information Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America, 2 Department of Genetics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America 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 imp