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Advancing the State of the Art in Computational Gene Prediction

  • Conference paper
Book cover Knowledge Discovery and Emergent Complexity in Bioinformatics (KDECB 2006)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 4366))

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Abstract

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.

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Authors and Affiliations

  1. Center for Bioinformatics and Computational Biology, Institute for Genome Sciences and Policy, Duke University, 101 Science Drive, Durham, NC 27708, USA

    William H. Majoros & Uwe Ohler

Authors
  1. William H. Majoros
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  2. Uwe Ohler
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Karl Tuyls Ronald Westra Yvan Saeys Ann Nowé

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© 2007 Springer Berlin Heidelberg

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Majoros, W.H., Ohler, U. (2007). Advancing the State of the Art in Computational Gene Prediction. In: Tuyls, K., Westra, R., Saeys, Y., Nowé, A. (eds) Knowledge Discovery and Emergent Complexity in Bioinformatics. KDECB 2006. Lecture Notes in Computer Science(), vol 4366. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71037-0_6

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  • DOI: https://doi.org/10.1007/978-3-540-71037-0_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-71036-3

  • Online ISBN: 978-3-540-71037-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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