Classifying G-protein coupled receptors with support vector machines

By Rachel Karchin, Kevin J. Karplus, David Haussler. Bioinform., 2002

297 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/ff1145282e95d1852e60f5f64125c17c111e5dd6

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In Molecular Biology

Citations in the corpus

Update of PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence (2006, background): “From H1ði Þ and H2ði Þ defined in Equation (1) and (2), the hydrophobicity and hydrophilicity correlation functions are defined (32), respectively, as:”
Classification of Nuclear Receptors Based on Amino Acid Composition and Dipeptide Composition* (2004, unknown): “SVM has been shown to perform well in multiple areas of biological analysis, including classification of G-protein coupled receptors (GPCRs) (14), enzyme families (15), analysis of proteins functions and types (16-17) and prediction of RNA binding proteins (18).”
Subsequence-based feature map for protein function classification (2008, methodology): “…are then arranged as feature vectors and used s input to classification techniques such as artificial neural netorks (ANNs) or support vector machines (SVMs) (Duda et l., 2000; King et al., 2000; Pasquier et al., 2001; Jensen et l., 2002; Cai et al., 2003; Karchin et al., 2002; Cheng et al., 005).”
Graphical Models of Residue Coupling in Protein Families (2008, methodology): “Although some SVM approaches have been shown to obtain similar accuracy for GPCRs [4], [14], as we illustrate below, our graphical models are transparent, making apparent the key 194 IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, VOL.”
A Systematic Prediction of Multiple Drug-Target Interactions from Chemical, Genomic, and Pharmacological Data (2012, background): “If the unrelated receptors do not possess similar ligands in the learning dataset, then the protein encoding (structural and physicochemical properties derived from their primary sequence) plays a key role for the unrelated targets identification, which have been well applied for the prediction of p”
Graphical models of residue coupling in protein families (2005, methodology): “Although some SVM approaches have been shown to obtain similar accuracy for GPCRs [4], [14], as we illustrate below, our graphical models are transparent, making apparent the key differential aspects (conservation and coupling) in a manner readily permitting biological study.”