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Items: 1 to 20 of 77

1.

Homology-based inference sets the bar high for protein function prediction.

Hamp T, Kassner R, Seemayer S, Vicedo E, Schaefer C, Achten D, Auer F, Boehm A, Braun T, Hecht M, Heron M, Hönigschmid P, Hopf TA, Kaufmann S, Kiening M, Krompass D, Landerer C, Mahlich Y, Roos M, Rost B.

BMC Bioinformatics. 2013;14 Suppl 3:S7. doi: 10.1186/1471-2105-14-S3-S7.

2.

Protein function prediction using text-based features extracted from the biomedical literature: the CAFA challenge.

Wong A, Shatkay H.

BMC Bioinformatics. 2013;14 Suppl 3:S14. doi: 10.1186/1471-2105-14-S3-S14.

3.

Prediction of protein structural classes for low-homology sequences based on predicted secondary structure.

Yang JY, Peng ZL, Chen X.

BMC Bioinformatics. 2010 Jan 18;11 Suppl 1:S9. doi: 10.1186/1471-2105-11-S1-S9.

4.

MS-kNN: protein function prediction by integrating multiple data sources.

Lan L, Djuric N, Guo Y, Vucetic S.

BMC Bioinformatics. 2013;14 Suppl 3:S8. doi: 10.1186/1471-2105-14-S3-S8.

5.

Prediction of protein subcellular localization.

Yu CS, Chen YC, Lu CH, Hwang JK.

Proteins. 2006 Aug 15;64(3):643-51.

PMID:
16752418
6.

Compression-based classification of biological sequences and structures via the Universal Similarity Metric: experimental assessment.

Ferragina P, Giancarlo R, Greco V, Manzini G, Valiente G.

BMC Bioinformatics. 2007 Jul 13;8:252.

7.

Protein subcellular localization prediction based on compartment-specific features and structure conservation.

Su EC, Chiu HS, Lo A, Hwang JK, Sung TY, Hsu WL.

BMC Bioinformatics. 2007 Sep 8;8:330.

8.

An expanded evaluation of protein function prediction methods shows an improvement in accuracy.

Jiang Y, Oron TR, Clark WT, Bankapur AR, D'Andrea D, Lepore R, Funk CS, Kahanda I, Verspoor KM, Ben-Hur A, Koo da CE, Penfold-Brown D, Shasha D, Youngs N, Bonneau R, Lin A, Sahraeian SM, Martelli PL, Profiti G, Casadio R, Cao R, Zhong Z, Cheng J, Altenhoff A, Skunca N, Dessimoz C, Dogan T, Hakala K, Kaewphan S, Mehryary F, Salakoski T, Ginter F, Fang H, Smithers B, Oates M, Gough J, Törönen P, Koskinen P, Holm L, Chen CT, Hsu WL, Bryson K, Cozzetto D, Minneci F, Jones DT, Chapman S, Bkc D, Khan IK, Kihara D, Ofer D, Rappoport N, Stern A, Cibrian-Uhalte E, Denny P, Foulger RE, Hieta R, Legge D, Lovering RC, Magrane M, Melidoni AN, Mutowo-Meullenet P, Pichler K, Shypitsyna A, Li B, Zakeri P, ElShal S, Tranchevent LC, Das S, Dawson NL, Lee D, Lees JG, Sillitoe I, Bhat P, Nepusz T, Romero AE, Sasidharan R, Yang H, Paccanaro A, Gillis J, Sedeño-Cortés AE, Pavlidis P, Feng S, Cejuela JM, Goldberg T, Hamp T, Richter L, Salamov A, Gabaldon T, Marcet-Houben M, Supek F, Gong Q, Ning W, Zhou Y, Tian W, Falda M, Fontana P, Lavezzo E, Toppo S, Ferrari C, Giollo M, Piovesan D, Tosatto SC, Del Pozo A, Fernández JM, Maietta P, Valencia A, Tress ML, Benso A, Di Carlo S, Politano G, Savino A, Rehman HU, Re M, Mesiti M, Valentini G, Bargsten JW, van Dijk AD, Gemovic B, Glisic S, Perovic V, Veljkovic V, Veljkovic N, Almeida-E-Silva DC, Vencio RZ, Sharan M, Vogel J, Kansakar L, Zhang S, Vucetic S, Wang Z, Sternberg MJ, Wass MN, Huntley RP, Martin MJ, O'Donovan C, Robinson PN, Moreau Y, Tramontano A, Babbitt PC, Brenner SE, Linial M, Orengo CA, Rost B, Greene CS, Mooney SD, Friedberg I, Radivojac P.

Genome Biol. 2016 Sep 7;17(1):184. doi: 10.1186/s13059-016-1037-6.

9.

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.

10.

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
11.

Exploiting multi-layered information to iteratively predict protein functions.

Zhu W, Hou J, Chen YP.

Math Biosci. 2012 Apr;236(2):108-16. doi: 10.1016/j.mbs.2012.02.004.

PMID:
22391459
12.

In-depth performance evaluation of PFP and ESG sequence-based function prediction methods in CAFA 2011 experiment.

Chitale M, Khan IK, Kihara D.

BMC Bioinformatics. 2013;14 Suppl 3:S2. doi: 10.1186/1471-2105-14-S3-S2.

13.

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.

14.

Automatic annotation of protein function based on family identification.

Abascal F, Valencia A.

Proteins. 2003 Nov 15;53(3):683-92.

PMID:
14579359
15.

Using inferred residue contacts to distinguish between correct and incorrect protein models.

Miller CS, Eisenberg D.

Bioinformatics. 2008 Jul 15;24(14):1575-82. doi: 10.1093/bioinformatics/btn248.

16.

FunFOLD: an improved automated method for the prediction of ligand binding residues using 3D models of proteins.

Roche DB, Tetchner SJ, McGuffin LJ.

BMC Bioinformatics. 2011 May 16;12:160. doi: 10.1186/1471-2105-12-160.

17.

HomPPI: a class of sequence homology based protein-protein interface prediction methods.

Xue LC, Dobbs D, Honavar V.

BMC Bioinformatics. 2011 Jun 17;12:244. doi: 10.1186/1471-2105-12-244.

18.
19.

Solvent and lipid accessibility prediction as a basis for model quality assessment in soluble and membrane proteins.

Phatak M, Adamczak R, Cao B, Wagner M, Meller J.

Curr Protein Pept Sci. 2011 Sep;12(6):563-73.

PMID:
21787302
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