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A new approach using distance matrix image to predict Gprotein-coupled receptor functional classes

Elsevier Ltd
DOI: 10.1016/j.egypro.2011.12.533
  • G-Protein-Coupled Receptor
  • Distance Matrix
  • Geometric Moment
  • Fuzzy K-Nearest Neighbor Algorithm
  • Jackknife Cross-Validation
  • Biology
  • Mathematics


Abstract G-protein coupled receptors (GPCRs) play a key role in diverse physiological processes and are the targets of over half of the marketed drugs. Using the pseudo amino acid composition (PseAAC) to represent the sample of a protein can incorporate a considerable amount of sequence pattern information so as to improve the prediction quality for its structural or functional classification. In this paper, the protein distance matrix image (DMI) is introduced, from which the geometric moments are extracted to represent the samples of proteins as pseudo amino acid composition. It was demonstrated thru the Leave-one-out cross-validation that the overall success rate by the new approach was very effective. The success rates thus obtained on a previously constructed benchmark dataset are quite promising, which indicates that our method can be a useful tool for predicting functional types of GPCRs.

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