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» Support Vector Training of Protein Alignment Models
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BMCBI
2008
88views more  BMCBI 2008»
13 years 7 months ago
Use of machine learning algorithms to classify binary protein sequences as highly-designable or poorly-designable
Background: By using a standard Support Vector Machine (SVM) with a Sequential Minimal Optimization (SMO) method of training, Na
Myron Peto, Andrzej Kloczkowski, Vasant Honavar, R...
ISMB
1993
13 years 8 months ago
Using Dirichlet Mixture Priors to Derive Hidden Markov Models for Protein Families
A Bayesian method for estimating the amino acid distributions in the states of a hidden Markov model (HMM) for a protein familyor the columns of a multiple alignment of that famil...
Michael Brown, Richard Hughey, Anders Krogh, I. Sa...
DMKD
2003
ACM
110views Data Mining» more  DMKD 2003»
14 years 22 days ago
Weave amino acid sequences for protein secondary structure prediction
Given a known protein sequence, predicting its secondary structure can help understand its three-dimensional (tertiary) structure, i.e., the folding. In this paper, we present an ...
Xiaochun Yang, Bin Wang
BIOINFORMATICS
2005
140views more  BIOINFORMATICS 2005»
13 years 7 months ago
Profile-based direct kernels for remote homology detection and fold recognition
Motivation: Remote homology detection between protein sequences is a central problem in computational biology. Supervised learning algorithms based on support vector machines are ...
Huzefa Rangwala, George Karypis
JCB
2008
106views more  JCB 2008»
13 years 7 months ago
Statistics of Random Protein Superpositions: p-Values for Pairwise Structure Alignment
Quantification of statistical significance is essential for the interpretation of protein structural similarity. To address this, a random model for protein structure comparison w...
James O. Wrabl, Nick V. Grishin