Abstract
To improve the performance of computational gene annotation programs, we introduced the well known interpolated Markov Chain (IMC) technology to the class Hidden Markov models (CHMM). CHMM was applied in one of the best eukaryotic gene prediction systems: HMMgene. The conditional Maximum Likelihood estimation (CMLE) algorithm was educed to estimate the interpolation parameters. The resulting gene prediction program improves exon level sensitivity by 3% and specificity by about 1% compared to HMMgene as trained and tested on some standard human DNA sequence dataset.
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© 2006 Springer-Verlag Berlin Heidelberg
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Zhu, H., Wang, J., Yang, Z., Song, Y. (2006). Interpolated Hidden Markov Models Estimated Using Conditional ML for Eukaryotic Gene Annotation. In: Huang, DS., Li, K., Irwin, G.W. (eds) Computational Intelligence and Bioinformatics. ICIC 2006. Lecture Notes in Computer Science(), vol 4115. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816102_29
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DOI: https://doi.org/10.1007/11816102_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37277-6
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