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BMCBI
2005
141views more  BMCBI 2005»
13 years 9 months ago
A method for the prediction of GPCRs coupling specificity to G-proteins using refined profile Hidden Markov Models
Background: G- Protein coupled receptors (GPCRs) comprise the largest group of eukaryotic cell surface receptors with great pharmacological interest. A broad range of native ligan...
Nikolaos G. Sgourakis, Pantelis G. Bagos, Panagiot...
BMCBI
2010
123views more  BMCBI 2010»
13 years 4 months ago
Predicting conserved protein motifs with Sub-HMMs
Background: Profile HMMs (hidden Markov models) provide effective methods for modeling the conserved regions of protein families. A limitation of the resulting domain models is th...
Kevin Horan, Christian R. Shelton, Thomas Girke
ICPR
2010
IEEE
14 years 29 days ago
Spatial Representation for Efficient Sequence Classification
We present a general, simple feature representation of sequences that allows efficient inexact matching, comparison and classification of sequential data. This approach, recently ...
Pavel Kuksa, Vladimir Pavlovic
FUZZIEEE
2007
IEEE
14 years 3 months ago
Mining and Predicting CpG islands
— A DNA sequence can be described as a string composed of four symbols: A, T, C and G. Each symbol represents a chemically distinct nucleotide molecule. Combinations of two nucle...
Christopher Previti, Oscar Harari, Coral del Val
BMCBI
2011
13 years 1 months ago
GPCR-SSFE: A comprehensive database of G-protein-coupled receptor template predictions and homology models
Background: G protein-coupled receptors (GPCRs) transduce a wide variety of extracellular signals to within the cell and therefore have a key role in regulating cell activity and ...
Catherine L. Worth, Annika Kreuchwig, Gunnar Klein...