相似文献
1
Predicting the coupling specificity of GPCRs to G-proteins by support vector machines.
Genomics Proteomics Bioinformatics. 2005 Nov;3(4):247-51. doi: 10.1016/s1672-0229(05)03035-4.
2
Predicting G-protein coupled receptors-G-protein coupling specificity based on autocross-covariance transform.
Proteins. 2006 Oct 1;65(1):55-60. doi: 10.1002/prot.21097.
3
Classification of G proteins and prediction of GPCRs-G proteins coupling specificity using continuous wavelet transform and information theory.
Amino Acids. 2012 Aug;43(2):793-804. doi: 10.1007/s00726-011-1133-6. Epub 2011 Nov 16.
4
Prediction and classification of human G-protein coupled receptors based on support vector machines.
Genomics Proteomics Bioinformatics. 2005 Nov;3(4):242-6. doi: 10.1016/s1672-0229(05)03034-2.
5
Statistical analysis and prediction of functional residues effective for GPCR-G-protein coupling selectivity.
Protein Eng Des Sel. 2006 Jun;19(6):277-83. doi: 10.1093/protein/gzl010. Epub 2006 Mar 24.
6
Identifying G-protein coupled receptors using weighted Levenshtein distance and the nearest neighbor method.
Genomics Proteomics Bioinformatics. 2005 Nov;3(4):252-7. doi: 10.1016/s1672-0229(05)03036-6.
7
A method for the prediction of GPCRs coupling specificity to G-proteins using refined profile Hidden Markov Models.
BMC Bioinformatics. 2005 Apr 22;6:104. doi: 10.1186/1471-2105-6-104.
8
GPCR-MPredictor: multi-level prediction of G protein-coupled receptors using genetic ensemble.
Amino Acids. 2012 May;42(5):1809-23. doi: 10.1007/s00726-011-0902-6. Epub 2011 Apr 20.
9
PRECOG: PREdicting COupling probabilities of G-protein coupled receptors.
Nucleic Acids Res. 2019 Jul 2;47(W1):W395-W401. doi: 10.1093/nar/gkz392.
10
A naive Bayes model to predict coupling between seven transmembrane domain receptors and G-proteins.
Bioinformatics. 2003 Jan 22;19(2):234-40. doi: 10.1093/bioinformatics/19.2.234.
引用本文的文献
1
A novel approach for prediction of tacrolimus blood concentration in liver transplantation patients in the intensive care unit through support vector regression.
Crit Care. 2007;11(4):R83. doi: 10.1186/cc6081.
本文引用的文献
1
A method for the prediction of GPCRs coupling specificity to G-proteins using refined profile Hidden Markov Models.
BMC Bioinformatics. 2005 Apr 22;6:104. doi: 10.1186/1471-2105-6-104.
2
A database for G proteins and their interaction with GPCRs.
BMC Bioinformatics. 2004 Dec 24;5:208. doi: 10.1186/1471-2105-5-208.
3
Predicting GPCR-G-protein coupling using hidden Markov models.
Bioinformatics. 2004 Dec 12;20(18):3490-9. doi: 10.1093/bioinformatics/bth434. Epub 2004 Aug 5.
4
ConPred II: a consensus prediction method for obtaining transmembrane topology models with high reliability.
Nucleic Acids Res. 2004 Jul 1;32(Web Server issue):W390-3. doi: 10.1093/nar/gkh380.
5
Enzyme family classification by support vector machines.
Proteins. 2004 Apr 1;55(1):66-76. doi: 10.1002/prot.20045.
6
Depicting a protein's two faces: GPCR classification by phylogenetic tree-based HMMs.
FEBS Lett. 2003 Nov 6;554(1-2):95-9. doi: 10.1016/s0014-5793(03)01112-8.
7
G protein selectivity is regulated by multiple intracellular regions of GPCRs.
Neurosignals. 2003 Jan-Feb;12(1):1-12. doi: 10.1159/000068914.
8
A naive Bayes model to predict coupling between seven transmembrane domain receptors and G-proteins.
Bioinformatics. 2003 Jan 22;19(2):234-40. doi: 10.1093/bioinformatics/19.2.234.
9
A study on the correlation of G-protein-coupled receptor types with amino acid composition.
Protein Eng. 2002 Sep;15(9):713-5. doi: 10.1093/protein/15.9.713.
10
The spectrum kernel: a string kernel for SVM protein classification.
Pac Symp Biocomput. 2002:564-75.
Zotero 插件