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JCB
2006
215views more  JCB 2006»
14 years 9 months ago
Protein Fold Recognition Using Segmentation Conditional Random Fields (SCRFs)
Protein fold recognition is an important step towards understanding protein three-dimensional structures and their functions. A conditional graphical model, i.e., segmentation con...
Yan Liu 0002, Jaime G. Carbonell, Peter Weigele, V...
RECOMB
2003
Springer
15 years 10 months ago
Combining phylogenetic and hidden Markov models in biosequence analysis
A few models have appeared in recent years that consider not only the way substitutions occur through evolutionary history at each site of a genome, but also the way the process c...
Adam C. Siepel, David Haussler
BMCBI
2010
117views more  BMCBI 2010»
14 years 9 months ago
New decoding algorithms for Hidden Markov Models using distance measures on labellings
Background: Existing hidden Markov model decoding algorithms do not focus on approximately identifying the sequence feature boundaries. Results: We give a set of algorithms to com...
Daniel G. Brown 0001, Jakub Truszkowski
NAR
2006
82views more  NAR 2006»
14 years 9 months ago
PROFtmb: a web server for predicting bacterial transmembrane beta barrel proteins
PROFtmb predicts transmembrane beta-barrel (TMB) proteins in Gram-negative bacteria. For each query protein, PROFtmb provides both a Z-value indicating that the protein actually c...
Henry R. Bigelow, Burkhard Rost
BMCBI
2002
108views more  BMCBI 2002»
14 years 9 months ago
A memory-efficient dynamic programming algorithm for optimal alignment of a sequence to an RNA secondary structure
Background: Covariance models (CMs) are probabilistic models of RNA secondary structure, analogous to profile hidden Markov models of linear sequence. The dynamic programming algo...
Sean R. Eddy