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    BMCBI
    2010
    151views more  BMCBI 2010»
    Classification of G-protein coupled receptors based on support vector machine with maximum relevance minimum redundancy and gene
    13 years 7 months ago
    Classification of G-protein coupled receptors based on support vector machine with maximum relevance minimum redundancy and gene
    Download  www.biomedcentral.com
    Background: Because a priori knowledge about function of G protein-coupled receptors (GPCRs) can provide useful information to pharmaceutical research, the determination of their ...
    Zhanchao Li, Xuan Zhou, Zong Dai, Xiaoyong Zou
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    ALMOB
    2006
    155views more  ALMOB 2006»
    A phylogenetic generalized hidden Markov model for predicting alternatively spliced exons
    13 years 7 months ago
    A phylogenetic generalized hidden Markov model for predicting alternatively spliced exons
    Download  www.almob.org
    Background: An important challenge in eukaryotic gene prediction is accurate identification of alternatively spliced exons. Functional transcripts can go undetected in gene expres...
    Jonathan E. Allen, Steven L. Salzberg
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    ISMB
    1996
    155views Computational Biology» more  ISMB 1996»
    Gene Recognition in Cyanobacterium Genomic Sequence Data Using the Hidden Markov Model
    13 years 8 months ago
    Gene Recognition in Cyanobacterium Genomic Sequence Data Using the Hidden Markov Model
    Download  www.aaai.org
    We have developed a hidden Markov model (HMM)to detect the protein coding regions within one megabase contiguous sequence data, registered in a database called GenBankin eight ent...
    Tetsushi Yada, Makoto Hirosawa
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    ICPR
    2008
    IEEE
    134views Computer Vision» more  ICPR 2008»
    Embedding HMM's-based models in a Euclidean space: The topological hidden Markov models
    14 years 1 months ago
    Embedding HMM's-based models in a Euclidean space: The topological hidden Markov models
    Download  figment.cse.usf.edu
    One of the major limitations of HMM-based models is the inability to cope with topology: When applied to a visible observation (VO) sequence, HMM-based techniques have difficulty ...
    Djamel Bouchaffra
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    BMCBI
    2004
    208views more  BMCBI 2004»
    Using 3D Hidden Markov Models that explicitly represent spatial coordinates to model and compare protein structures
    13 years 7 months ago
    Using 3D Hidden Markov Models that explicitly represent spatial coordinates to model and compare protein structures
    Download  www.biomedcentral.com
    Background: Hidden Markov Models (HMMs) have proven very useful in computational biology for such applications as sequence pattern matching, gene-finding, and structure prediction...
    Vadim Alexandrov, Mark Gerstein
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