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Items: 1 to 20 of 97

1.

Protein hypersaline adaptation: insight from amino acids with machine learning algorithms.

Zhang G, Ge H.

Protein J. 2013 Apr;32(4):239-45. doi: 10.1007/s10930-013-9484-3.

PMID:
23592219
2.

Support vector machine with a Pearson VII function kernel for discriminating halophilic and non-halophilic proteins.

Zhang G, Ge H.

Comput Biol Chem. 2013 Oct;46:16-22. doi: 10.1016/j.compbiolchem.2013.05.001. Epub 2013 May 17.

3.

Stability of halophilic proteins: from dipeptide attributes to discrimination classifier.

Zhang G, Huihua G, Yi L.

Int J Biol Macromol. 2013 Feb;53:1-6. doi: 10.1016/j.ijbiomac.2012.10.031. Epub 2012 Nov 6.

PMID:
23142140
4.
5.

Haloadaptation: insights from comparative modeling studies of halophilic archaeal DHFRs.

Kastritis PL, Papandreou NC, Hamodrakas SJ.

Int J Biol Macromol. 2007 Oct 1;41(4):447-53. Epub 2007 Jun 19.

PMID:
17675150
7.

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.

8.

Machine learning study of classifiers trained with biophysiochemical properties of amino acids to predict fibril forming Peptide motifs.

Kumaran Nair SS, Subba Reddy NV, Hareesha KS.

Protein Pept Lett. 2012 Sep;19(9):917-23.

PMID:
22486618
9.
10.

Support vector machines for prediction and analysis of beta and gamma-turns in proteins.

Pham TH, Satou K, Ho TB.

J Bioinform Comput Biol. 2005 Apr;3(2):343-58.

PMID:
15852509
11.

Predicting the secondary structure of proteins using machine learning algorithms.

Camacho R, Ferreira R, Rosa N, Guimarães V, Fonseca NA, Costa VS, de Sousa M, Magalhães A.

Int J Data Min Bioinform. 2012;6(6):571-84.

PMID:
23356008
12.

Predicting protein secondary structure by a support vector machine based on a new coding scheme.

Wang LH, Liu J, Li YF, Zhou HB.

Genome Inform. 2004;15(2):181-90.

PMID:
15706504
13.

Human pol II promoter prediction: time series descriptors and machine learning.

Gangal R, Sharma P.

Nucleic Acids Res. 2005 Mar 1;33(4):1332-6. Print 2005. Erratum in: Nucleic Acids Res. 2005;33(13):4378.

14.

Protein-protein interaction site predictions with three-dimensional probability distributions of interacting atoms on protein surfaces.

Chen CT, Peng HP, Jian JW, Tsai KC, Chang JY, Yang EW, Chen JB, Ho SY, Hsu WL, Yang AS.

PLoS One. 2012;7(6):e37706. doi: 10.1371/journal.pone.0037706. Epub 2012 Jun 6.

15.
16.

A novel method based on physicochemical properties of amino acids and one class classification algorithm for disease gene identification.

Yousef A, Charkari NM.

J Biomed Inform. 2015 Aug;56:300-6. doi: 10.1016/j.jbi.2015.06.018. Epub 2015 Jul 2.

17.

Predicting RNA-binding sites of proteins using support vector machines and evolutionary information.

Cheng CW, Su EC, Hwang JK, Sung TY, Hsu WL.

BMC Bioinformatics. 2008 Dec 12;9 Suppl 12:S6. doi: 10.1186/1471-2105-9-S12-S6.

18.

Discrimination of mesophilic and thermophilic proteins using machine learning algorithms.

Gromiha MM, Suresh MX.

Proteins. 2008 Mar;70(4):1274-9.

PMID:
17876820
19.

Cellular automata and its applications in protein bioinformatics.

Xiao X, Wang P, Chou KC.

Curr Protein Pept Sci. 2011 Sep;12(6):508-19. Review.

PMID:
21787298
20.

Accurate prediction of stability changes in protein mutants by combining machine learning with structure based computational mutagenesis.

Masso M, Vaisman II.

Bioinformatics. 2008 Sep 15;24(18):2002-9. doi: 10.1093/bioinformatics/btn353. Epub 2008 Jul 16.

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