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» A Probabilistic Graphical Model for Ab Initio Folding
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BMCBI
2007
143views more  BMCBI 2007»
13 years 5 months ago
An adaptive bin framework search method for a beta-sheet protein homopolymer model
Background: The problem of protein structure prediction consists of predicting the functional or native structure of a protein given its linear sequence of amino acids. This probl...
Alena Shmygelska, Holger H. Hoos
BMCBI
2005
89views more  BMCBI 2005»
13 years 5 months ago
An empirical analysis of training protocols for probabilistic gene finders
Background: Generalized hidden Markov models (GHMMs) appear to be approaching acceptance as a de facto standard for state-of-the-art ab initio gene finding, as evidenced by the re...
William H. Majoros, Steven Salzberg
BMCBI
2004
166views more  BMCBI 2004»
13 years 5 months ago
Gene prediction using the Self-Organizing Map: automatic generation of multiple gene models
Background: Many current gene prediction methods use only one model to represent proteincoding regions in a genome, and so are less likely to predict the location of genes that ha...
Shaun Mahony, James O. McInerney, Terry J. Smith, ...
BMCBI
2006
160views more  BMCBI 2006»
13 years 5 months ago
Gene prediction in eukaryotes with a generalized hidden Markov model that uses hints from external sources
Background: In order to improve gene prediction, extrinsic evidence on the gene structure can be collected from various sources of information such as genome-genome comparisons an...
Mario Stanke, Oliver Schöffmann, Burkhard Mor...
BMCBI
2006
119views more  BMCBI 2006»
13 years 5 months ago
Hidden Markov Model Variants and their Application
Markov statistical methods may make it possible to develop an unsupervised learning process that can automatically identify genomic structure in prokaryotes in a comprehensive way...
Stephen Winters-Hilt