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» Support Vector Training of Protein Alignment Models
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BMCBI
2008
88views more  BMCBI 2008»
15 years 1 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
15 years 3 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»
15 years 7 months 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»
15 years 1 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»
15 years 1 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