Presentation 2003/6/19
Variational inference for estimating direction and position of sequence via HMM and its application to protein structure comparison
Tsuyoshi KATO, Koji TSUDA, Kentaro TOMII, Kiyoshi ASAI,
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Abstract(in English) In this paper, we present a novel algorithm for estimating the parameters of rigid-body transformation for sequence which contains the d-dimensional coordinates. The algorithm can be applied to determining the direction and position of protein structure. Usually the available sequences of proteins are rotated and shifted. It is necessary to correct the rotation and shift before analyzing protein structures. So we model the sequence via hidden Markov model (HMM) and develop the maximum likelihood estimation algorithm which infers the parameters of HMM and rigid-body transformation, simultaneously using variational method. The experimental results reveals the effectiveness of our method.
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Keyword(in English) rigid-body transformation / hidden Markov model / variational method / GEM algorithm / protein structure comparison
Paper # PRMU2003-31
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Committee PRMU
Conference Date 2003/6/19(1days)
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Registration To Pattern Recognition and Media Understanding (PRMU)
Language ENG
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Variational inference for estimating direction and position of sequence via HMM and its application to protein structure comparison
Sub Title (in English)
Keyword(1) rigid-body transformation
Keyword(2) hidden Markov model
Keyword(3) variational method
Keyword(4) GEM algorithm
Keyword(5) protein structure comparison
1st Author's Name Tsuyoshi KATO
1st Author's Affiliation AIST Computational Biology Research Center()
2nd Author's Name Koji TSUDA
2nd Author's Affiliation AIST Computational Biology Research Center:Max Planck Institute for Biological Cybernetics
3rd Author's Name Kentaro TOMII
3rd Author's Affiliation AIST Computational Biology Research Center
4th Author's Name Kiyoshi ASAI
4th Author's Affiliation AIST Computational Biology Research Center:Graduate School of Frontier Sciences, The University of Tokyo
Date 2003/6/19
Paper # PRMU2003-31
Volume (vol) vol.103
Number (no) 150
Page pp.pp.-
#Pages 6
Date of Issue