Generalized Hidden Markov Model
Mostrando 1-8 de 8 artigos, teses e dissertações.
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1. Modelo mel-cepstral generalizado para envoltória espectral de fala / Mel-generalized cepstral model for speech spectral envelope
A análise Mel-Cepstral Generalizada (MGC) corresponde a uma abordagem para estimação de envoltória espectral de fala que unifica as análises LPC, Mel-LPC, Cepstral e Mel-Cepstral. A forma funcional do modelo MGC varia continuamente com dois parâmetros reais γ e α, possibilitando que o modelo assuma diferentes características. A flexibilidade
IBICT - Instituto Brasileiro de Informação em Ciência e Tecnologia. Publicado em: 27/10/2010
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2. MYOP: um arcabouço para predição de genes ab initio" / MYOP: A framework for building ab initio gene predictors
The demand for efficient approaches for the gene structure prediction has motivated the implementation of different programs. In this work, we have analyzed successful programs that apply the probabilistic approach. We have observed similarities between different implementations, the same mathematical framework called generalized hidden Markov chain (GHMM) i
Publicado em: 2007
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3. Efficient decoding algorithms for generalized hidden Markov model gene finders
BioMed Central.
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4. SLAM: Cross-Species Gene Finding and Alignment with a Generalized Pair Hidden Markov Model
Comparative-based gene recognition is driven by the principle that conserved regions between related organisms are more likely than divergent regions to be coding. We describe a probabilistic framework for gene structure and alignment that can be used to simultaneously find both the gene structure and alignment of two syntenic genomic regions. A key feature
Cold Spring Harbor Laboratory Press.
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5. GlimmerM, Exonomy and Unveil: three ab initio eukaryotic genefinders
We present three programs for ab initio gene prediction in eukaryotes: Exonomy, Unveil and GlimmerM. Exonomy is a 23-state Generalized Hidden Markov Model (GHMM), Unveil is a 283-state standard Hidden Markov Model (HMM) and GlimmerM is a previously-described genefinder which utilizes decision trees and Interpolated Markov Models (IMMs). All three are readily
Oxford University Press.
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6. AUGUSTUS: a web server for gene finding in eukaryotes
We present a www server for AUGUSTUS, a novel software program for ab initio gene prediction in eukaryotic genomic sequences. Our method is based on a generalized Hidden Markov Model with a new method for modeling the intron length distribution. This method allows approximation of the true intron length distribution more accurately than do existing programs.
Oxford University Press.
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7. Genie—Gene Finding in Drosophila melanogaster
A hidden Markov model-based gene-finding system called Genie was applied to the genomic Adh region in Drosophila melanogaster as a part of the Genome Annotation Assessment Project (GASP). Predictions from three versions of the Genie gene-finding system were submitted, one based on statistical properties of coding genes, a second included EST alignment inform
Cold Spring Harbor Laboratory Press.
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8. A probabilistic model of 3′ end formation in Caenorhabditis elegans
The 3′ ends of mRNAs terminate with a poly(A) tail. This post-transcriptional modification is directed by sequence features present in the 3′-untranslated region (3′-UTR). We have undertaken a computational analysis of 3′ end formation in Caenorhabditis elegans. By aligning cDNAs that diverge from genomic sequence at the poly(A) tract, we accurately
Oxford University Press.