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Items: 1 to 20 of 107

1.

TargetATPsite: a template-free method for ATP-binding sites prediction with residue evolution image sparse representation and classifier ensemble.

Yu DJ, Hu J, Huang Y, Shen HB, Qi Y, Tang ZM, Yang JY.

J Comput Chem. 2013 Apr 30;34(11):974-85. doi: 10.1002/jcc.23219. Epub 2013 Jan 3.

2.

Protein-chemical interaction prediction via kernelized sparse learning SVM.

Shi Y, Zhang X, Liao X, Lin G, Schuurmans D.

Pac Symp Biocomput. 2013:41-52.

3.

Designing template-free predictor for targeting protein-ligand binding sites with classifier ensemble and spatial clustering.

Yu DJ, Hu J, Yang J, Shen HB, Tang J, Yang JY.

IEEE/ACM Trans Comput Biol Bioinform. 2013 Jul-Aug;10(4):994-1008. doi: 10.1109/TCBB.2013.104.

PMID:
24334392
4.

Identification of ATP binding residues of a protein from its primary sequence.

Chauhan JS, Mishra NK, Raghava GP.

BMC Bioinformatics. 2009 Dec 19;10:434. doi: 10.1186/1471-2105-10-434.

5.

FRKAS: knowledge acquisition using a fuzzy rule base approach to insight of DNA-binding domains/proteins.

Huang HL, Chang FL, Ho SJ, Shu LS, Huang WL, Ho SY.

Protein Pept Lett. 2013 Mar;20(3):299-308.

PMID:
22591472
6.

Prediction of protein binding sites in protein structures using hidden Markov support vector machine.

Liu B, Wang X, Lin L, Tang B, Dong Q, Wang X.

BMC Bioinformatics. 2009 Nov 20;10:381. doi: 10.1186/1471-2105-10-381.

7.

Integrating statistical predictions and experimental verifications for enhancing protein-chemical interaction predictions in virtual screening.

Nagamine N, Shirakawa T, Minato Y, Torii K, Kobayashi H, Imoto M, Sakakibara Y.

PLoS Comput Biol. 2009 Jun;5(6):e1000397. doi: 10.1371/journal.pcbi.1000397. Epub 2009 Jun 5.

8.

Prediction of nuclear proteins using nuclear translocation signals proposed by probabilistic latent semantic indexing.

Su EC, Chang JM, Cheng CW, Sung TY, Hsu WL.

BMC Bioinformatics. 2012;13 Suppl 17:S13. doi: 10.1186/1471-2105-13-S17-S13. Epub 2012 Dec 13.

9.

LIBRUS: combined machine learning and homology information for sequence-based ligand-binding residue prediction.

Kauffman C, Karypis G.

Bioinformatics. 2009 Dec 1;25(23):3099-107. doi: 10.1093/bioinformatics/btp561. Epub 2009 Sep 28.

10.

Prediction of protein-protein interaction sites using an ensemble method.

Deng L, Guan J, Dong Q, Zhou S.

BMC Bioinformatics. 2009 Dec 16;10:426. doi: 10.1186/1471-2105-10-426.

11.

GPCR-MPredictor: multi-level prediction of G protein-coupled receptors using genetic ensemble.

Naveed M, Khan A.

Amino Acids. 2012 May;42(5):1809-23. doi: 10.1007/s00726-011-0902-6. Epub 2011 Apr 20. Erratum in: Amino Acids. 2012 May;42(5):1825. Khan, Asif Ullah [corrected to Khan, Asifullah].

PMID:
21505826
12.

SVM prediction of ligand-binding sites in bacterial lipoproteins employing shape and physio-chemical descriptors.

Kadam K, Prabhakar P, Jayaraman VK.

Protein Pept Lett. 2012 Nov;19(11):1155-62.

PMID:
22587788
13.

Prediction-based fingerprints of protein-protein interactions.

Porollo A, Meller J.

Proteins. 2007 Feb 15;66(3):630-45.

PMID:
17152079
14.
15.
16.

Disulfide connectivity prediction based on structural information without a prior knowledge of the bonding state of cysteines.

Lin HH, Hsu JC, Hsu YN, Pan RH, Chen YF, Tseng LY.

Comput Biol Med. 2013 Nov;43(11):1941-8. doi: 10.1016/j.compbiomed.2013.09.008. Epub 2013 Sep 18.

PMID:
24209939
17.

Predicting protein-ATP binding sites from primary sequence through fusing bi-profile sampling of multi-view features.

Zhang YN, Yu DJ, Li SS, Fan YX, Huang Y, Shen HB.

BMC Bioinformatics. 2012 May 31;13:118. doi: 10.1186/1471-2105-13-118.

18.

Prediction of nicotinamide adenine dinucleotide interacting sites based on ensemble support vector machine.

Wang X, Wang CC, Zhang YQ, Mi G, Zhang J, Li ML.

Protein Pept Lett. 2012 May;19(5):559-66.

PMID:
22316310
19.

A new protein binding pocket similarity measure based on comparison of clouds of atoms in 3D: application to ligand prediction.

Hoffmann B, Zaslavskiy M, Vert JP, Stoven V.

BMC Bioinformatics. 2010 Feb 22;11:99. doi: 10.1186/1471-2105-11-99.

20.

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