Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
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
Two Methods for Improving Performance of a HMM and their Application for Gene Finding
Proceedings of the 5th International Conference on Intelligent Systems for Molecular Biology
Computational identification of evolutionarily conserved exons
RECOMB '04 Proceedings of the eighth annual international conference on Resaerch in computational molecular biology
Using multiple alignments to improve gene prediction
RECOMB'05 Proceedings of the 9th Annual international conference on Research in Computational Molecular Biology
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Current methods for computationally predicting the locations and intron-exon structures of protein-coding genes in eukaryotic DNA are largely based on probabilistic, state-based generative models such as hidden Markov models and their various extensions. Unfortunately, little attention has been paid to the optimality of these models for the gene-parsing problem. Furthermore, as the prevalence of alternative splicing in human genes becomes more apparent, the "one gene, one parse" discipline endorsed by virtually all current gene-finding systems becomes less attractive from a biomedical perspective. Because our ability to accurately identify all the isoforms of each gene in the genome is of direct importance to biomedicine, our ability to improve gene-finding accuracy both for human and non-human DNA clearly has a potential to significantly impact human health. In this paper we review current methods and suggest a number of possible directions for further research that may alleviate some of these problems and ultimately lead to better and more useful gene predictions.