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    BMCBI
    2006
    143views more  BMCBI 2006»
    Application of protein structure alignments to iterated hidden Markov model protocols for structure prediction
    13 years 9 months ago
    Application of protein structure alignments to iterated hidden Markov model protocols for structure prediction
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    Background: One of the most powerful methods for the prediction of protein structure from sequence information alone is the iterative construction of profile-type models. Because ...
    Eric D. Scheeff, Philip E. Bourne
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    BMCBI
    2006
    150views more  BMCBI 2006»
    Predicting protein subcellular locations using hierarchical ensemble of Bayesian classifiers based on Markov chains
    13 years 9 months ago
    Predicting protein subcellular locations using hierarchical ensemble of Bayesian classifiers based on Markov chains
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    Background: The subcellular location of a protein is closely related to its function. It would be worthwhile to develop a method to predict the subcellular location for a given pr...
    Alla Bulashevska, Roland Eils
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    TCSV
    2008
    177views more  TCSV 2008»
    An ICA Mixture Hidden Markov Model for Video Content Analysis
    13 years 9 months ago
    An ICA Mixture Hidden Markov Model for Video Content Analysis
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    In this paper, a new theoretical framework based on hidden Markov model (HMM) and independent component analysis (ICA) mixture model is presented for content analysis of video, nam...
    Jian Zhou, Xiao-Ping Zhang
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    TIP
    2008
    133views more  TIP 2008»
    A Recursive Model-Reduction Method for Approximate Inference in Gaussian Markov Random Fields
    13 years 9 months ago
    A Recursive Model-Reduction Method for Approximate Inference in Gaussian Markov Random Fields
    Download  projects.csail.mit.edu
    This paper presents recursive cavity modeling--a principled, tractable approach to approximate, near-optimal inference for large Gauss-Markov random fields. The main idea is to su...
    Jason K. Johnson, Alan S. Willsky
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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 9 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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