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Items: 1 to 20 of 155

1.

Neural network and SVM classifiers accurately predict lipid binding proteins, irrespective of sequence homology.

Bakhtiarizadeh MR, Moradi-Shahrbabak M, Ebrahimi M, Ebrahimie E.

J Theor Biol. 2014 Sep 7;356:213-22. doi: 10.1016/j.jtbi.2014.04.040. Epub 2014 May 10.

PMID:
24819464
2.

SVM-Fold: a tool for discriminative multi-class protein fold and superfamily recognition.

Melvin I, Ie E, Kuang R, Weston J, Stafford WN, Leslie C.

BMC Bioinformatics. 2007 May 22;8 Suppl 4:S2.

3.

Signal peptide discrimination and cleavage site identification using SVM and NN.

Kazemian HB, Yusuf SA, White K.

Comput Biol Med. 2014 Feb;45:98-110. doi: 10.1016/j.compbiomed.2013.11.017. Epub 2013 Dec 1.

PMID:
24480169
4.

Remote protein homology detection and fold recognition using two-layer support vector machine classifiers.

Muda HM, Saad P, Othman RM.

Comput Biol Med. 2011 Aug;41(8):687-99. doi: 10.1016/j.compbiomed.2011.06.004. Epub 2011 Jun 25.

PMID:
21704312
5.

SVM-HUSTLE--an iterative semi-supervised machine learning approach for pairwise protein remote homology detection.

Shah AR, Oehmen CS, Webb-Robertson BJ.

Bioinformatics. 2008 Mar 15;24(6):783-90. doi: 10.1093/bioinformatics/btn028. Epub 2008 Feb 1.

PMID:
18245127
6.

Prediction of the functional class of metal-binding proteins from sequence derived physicochemical properties by support vector machine approach.

Lin HH, Han LY, Zhang HL, Zheng CJ, Xie B, Cao ZW, Chen YZ.

BMC Bioinformatics. 2006 Dec 18;7 Suppl 5:S13.

7.

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.

8.

Predicting membrane protein types by fusing composite protein sequence features into pseudo amino acid composition.

Hayat M, Khan A.

J Theor Biol. 2011 Feb 21;271(1):10-7. doi: 10.1016/j.jtbi.2010.11.017. Epub 2010 Nov 24.

PMID:
21110985
9.

BhairPred: prediction of beta-hairpins in a protein from multiple alignment information using ANN and SVM techniques.

Kumar M, Bhasin M, Natt NK, Raghava GP.

Nucleic Acids Res. 2005 Jul 1;33(Web Server issue):W154-9.

10.
11.

Computer-assisted lip diagnosis on Traditional Chinese Medicine using multi-class support vector machines.

Li F, Zhao C, Xia Z, Wang Y, Zhou X, Li GZ.

BMC Complement Altern Med. 2012 Aug 16;12:127. doi: 10.1186/1472-6882-12-127.

12.

Efficient remote homology detection using local structure.

Hou Y, Hsu W, Lee ML, Bystroff C.

Bioinformatics. 2003 Nov 22;19(17):2294-301.

PMID:
14630658
13.

Prediction of the functional class of lipid binding proteins from sequence-derived properties irrespective of sequence similarity.

Lin HH, Han LY, Zhang HL, Zheng CJ, Xie B, Chen YZ.

J Lipid Res. 2006 Apr;47(4):824-31. Epub 2006 Jan 27.

14.

Improving protein secondary structure prediction using a multi-modal BP method.

Qu W, Sui H, Yang B, Qian W.

Comput Biol Med. 2011 Oct;41(10):946-59. doi: 10.1016/j.compbiomed.2011.08.005. Epub 2011 Aug 30.

PMID:
21880310
15.

SCPRED: accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequences.

Kurgan L, Cios K, Chen K.

BMC Bioinformatics. 2008 May 1;9:226. doi: 10.1186/1471-2105-9-226.

16.

Sequence-based prediction of protein-binding sites in DNA: comparative study of two SVM models.

Park B, Im J, Tuvshinjargal N, Lee W, Han K.

Comput Methods Programs Biomed. 2014 Nov;117(2):158-67. doi: 10.1016/j.cmpb.2014.07.009. Epub 2014 Aug 1.

PMID:
25113160
17.

The accurate prediction of protein family from amino acid sequence by measuring features of sequence fragments.

Hong H, Hong Q, Perkins R, Shi L, Fang H, Su Z, Dragan Y, Fuscoe JC, Tong W.

J Comput Biol. 2009 Dec;16(12):1671-88. doi: 10.1089/cmb.2008.0115.

PMID:
20047490
18.

Multi-class protein fold recognition using support vector machines and neural networks.

Ding CH, Dubchak I.

Bioinformatics. 2001 Apr;17(4):349-58.

PMID:
11301304
19.

Prediction of turn types in protein structure by machine-learning classifiers.

Meissner M, Koch O, Klebe G, Schneider G.

Proteins. 2009 Feb 1;74(2):344-52. doi: 10.1002/prot.22164.

PMID:
18618702
20.

Predicting protein structural class by SVM with class-wise optimized features and decision probabilities.

Anand A, Pugalenthi G, Suganthan PN.

J Theor Biol. 2008 Jul 21;253(2):375-80. doi: 10.1016/j.jtbi.2008.02.031. Epub 2008 Mar 4.

PMID:
18423492

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