GPCR‐CA: A cellular automaton image approach for predicting G‐protein–coupled receptor functional classes

@article{Xiao2009GPCRCAAC,
  title={GPCR‐CA: A cellular automaton image approach for predicting G‐protein–coupled receptor functional classes},
  author={Xuan Xiao and Pu Wang and Kuo-Chen Chou},
  journal={Journal of Computational Chemistry},
  year={2009},
  volume={30}
}
  • X. Xiao, Pu Wang, K. Chou
  • Published 15 July 2009
  • Medicine, Mathematics, Computer Science
  • Journal of Computational Chemistry
Given an uncharacterized protein sequence, how can we identify whether it is a G‐protein–coupled receptor (GPCR) or not? If it is, which functional family class does it belong to? It is important to address these questions because GPCRs are among the most frequent targets of therapeutic drugs and the information thus obtained is very useful for “comparative and evolutionary pharmacology,” a technique often used for drug development. Here, we present a web‐server predictor called “GPCR‐CA… Expand
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References

SHOWING 1-10 OF 110 REFERENCES
Prediction of G-protein-coupled receptor classes.
  • K. Chou
  • Biology, Medicine
  • Journal of proteome research
  • 2005
TLDR
The high success rate suggests that the CD predictor holds very high potential to become a useful tool for understanding the actions of drugs that target GPCRs and designing new medications with fewer side effects and greater efficacy. Expand
Classification of G-protein coupled receptors at four levels.
TLDR
A nearest neighbor method has been introduced to discriminate GPCRs from non-GPCRs and subsequently classify GPCR at four levels on the basis of amino acid composition and dipeptide composition of proteins. Expand
Bioinformatical analysis of G-protein-coupled receptors.
TLDR
The results indicate that the types of G-protein-coupled receptors are predictable to a considerable accurate extent if a good training data set can be established for such a goal. Expand
Prediction of protein cellular attributes using pseudo‐amino acid composition
  • K. Chou
  • Biology, Medicine
  • Proteins
  • 2001
TLDR
A remarkable improvement in prediction quality has been observed by using the pseudo‐amino acid composition and its mathematical framework and biochemical implication may also have a notable impact on improving the prediction quality of other protein features. Expand
Predicting protein structural classes with pseudo amino acid composition: an approach using geometric moments of cellular automaton image.
TLDR
The success rates obtained on a previously constructed benchmark dataset are quite promising, implying that the cellular automaton image can help to reveal some inherent and subtle features deeply hidden in a pile of long and complicated amino acid sequences. Expand
Using optimized evidence-theoretic K-nearest neighbor classifier and pseudo-amino acid composition to predict membrane protein types.
TLDR
A novel predictor, the so-called "optimized evidence-theoretic K-nearest neighbor" or "OET-KNN" classifier, was proposed and it was demonstrated via the self-consistency test, jackknife test, and independent dataset test that the new predictor, compared with many previous ones, yielded higher success rates in most cases. Expand
Euk-PLoc: an ensemble classifier for large-scale eukaryotic protein subcellular location prediction
TLDR
A novel ensemble hybridization classifier was developed by fusing many basic individual classifiers through a voting system engineered by the KNN (K-Nearest Neighbor) principle, which resulted in a powerful predictor of subcellular location of eukaryotic proteins. Expand
Predicting enzyme family class in a hybridization space
  • K. Chou, Yu-Dong Cai
  • Biology, Medicine
  • Protein science : a publication of the Protein Society
  • 2004
TLDR
By hybridizing the gene ontology and pseudo‐amino‐acid composition, a new method that is called GO‐PseAA predictor is introduced and operated in a hybridization space to develop an automated method that can be used to give fast answers to questions given the sequence of a protein. Expand
Application of Pseudo Amino Acid Composition for Predicting Protein Subcellular Location: Stochastic Signal Processing Approach
TLDR
The results thus obtained are quite encouraging and it is anticipated that the digital signal processing approach has been introduced to partially incorporate the sequence order effect and may serve as a useful vehicle for many other protein science areas as well. Expand
Predicting protein subcellular location by fusing multiple classifiers
TLDR
It is anticipated that the novel ensemble classifier may also become a very useful vehicle in classifying other attributes of proteins according to their sequences, such as membrane protein type, enzyme family/sub‐family, G‐protein coupled receptor (GPCR) type, and structural class, among many others. Expand
...
1
2
3
4
5
...