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Items: 1 to 20 of 113

1.

A sequence-based hybrid predictor for identifying conformationally ambivalent regions in proteins.

Liu YC, Yang MH, Lin WL, Huang CK, Oyang YJ.

BMC Genomics. 2009 Dec 3;10 Suppl 3:S22. doi: 10.1186/1471-2164-10-S3-S22.

2.
3.

Predicting protein disorder by analyzing amino acid sequence.

Yang JY, Yang MQ.

BMC Genomics. 2008 Sep 16;9 Suppl 2:S8. doi: 10.1186/1471-2164-9-S2-S8.

4.

Exploiting heterogeneous features to improve in silico prediction of peptide status - amyloidogenic or non-amyloidogenic.

Nair SS, Subba Reddy NV, Hareesha KS.

BMC Bioinformatics. 2011;12 Suppl 13:S21. doi: 10.1186/1471-2105-12-S13-S21.

5.

Predicting microRNA precursors with a generalized Gaussian components based density estimation algorithm.

Hsieh CH, Chang DT, Hsueh CH, Wu CY, Oyang YJ.

BMC Bioinformatics. 2010 Jan 18;11 Suppl 1:S52. doi: 10.1186/1471-2105-11-S1-S52.

6.
7.

DomNet: protein domain boundary prediction using enhanced general regression network and new profiles.

Yoo PD, Sikder AR, Taheri J, Zhou BB, Zomaya AY.

IEEE Trans Nanobioscience. 2008 Jun;7(2):172-81. doi: 10.1109/TNB.2008.2000747.

PMID:
18556265
8.

Prediction of disordered regions in proteins based on the meta approach.

Ishida T, Kinoshita K.

Bioinformatics. 2008 Jun 1;24(11):1344-8. doi: 10.1093/bioinformatics/btn195.

PMID:
18426805
9.

DNA-binding residues and binding mode prediction with binding-mechanism concerned models.

Huang YF, Huang CC, Liu YC, Oyang YJ, Huang CK.

BMC Genomics. 2009 Dec 3;10 Suppl 3:S23. doi: 10.1186/1471-2164-10-S3-S23.

10.

A comprehensive system for evaluation of remote sequence similarity detection.

Qi Y, Sadreyev RI, Wang Y, Kim BH, Grishin NV.

BMC Bioinformatics. 2007 Aug 28;8:314.

11.
12.

Length-dependent prediction of protein intrinsic disorder.

Peng K, Radivojac P, Vucetic S, Dunker AK, Obradovic Z.

BMC Bioinformatics. 2006 Apr 17;7:208.

13.

Sequence-based prediction of protein interaction sites with an integrative method.

Chen XW, Jeong JC.

Bioinformatics. 2009 Mar 1;25(5):585-91. doi: 10.1093/bioinformatics/btp039.

PMID:
19153136
14.

Statistical geometry based prediction of nonsynonymous SNP functional effects using random forest and neuro-fuzzy classifiers.

Barenboim M, Masso M, Vaisman II, Jamison DC.

Proteins. 2008 Jun;71(4):1930-9. doi: 10.1002/prot.21838.

PMID:
18186470
15.
16.

HYPROSP II--a knowledge-based hybrid method for protein secondary structure prediction based on local prediction confidence.

Lin HN, Chang JM, Wu KP, Sung TY, Hsu WL.

Bioinformatics. 2005 Aug 1;21(15):3227-33.

PMID:
15932901
17.

POODLE-L: a two-level SVM prediction system for reliably predicting long disordered regions.

Hirose S, Shimizu K, Kanai S, Kuroda Y, Noguchi T.

Bioinformatics. 2007 Aug 15;23(16):2046-53.

PMID:
17545177
18.

Large-scale prediction of long disordered regions in proteins using random forests.

Han P, Zhang X, Norton RS, Feng ZP.

BMC Bioinformatics. 2009 Jan 7;10:8. doi: 10.1186/1471-2105-10-8.

19.

ProClust: improved clustering of protein sequences with an extended graph-based approach.

Pipenbacher P, Schliep A, Schneckener S, Schönhuth A, Schomburg D, Schrader R.

Bioinformatics. 2002;18 Suppl 2:S182-91.

PMID:
12386002
20.

CooPPS: a system for the cooperative prediction of protein structures.

Palopoli L, Terracina G.

J Bioinform Comput Biol. 2004 Sep;2(3):471-95.

PMID:
15359422
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