311 related articles for article (PubMed ID: 12592033)
61. NetTurnP--neural network prediction of beta-turns by use of evolutionary information and predicted protein sequence features.
Petersen B; Lundegaard C; Petersen TN
PLoS One; 2010 Nov; 5(11):e15079. PubMed ID: 21152409
[TBL] [Abstract][Full Text] [Related]
62. Prediction of the transmembrane regions of beta-barrel membrane proteins with a neural network-based predictor.
Jacoboni I; Martelli PL; Fariselli P; De Pinto V; Casadio R
Protein Sci; 2001 Apr; 10(4):779-87. PubMed ID: 11274469
[TBL] [Abstract][Full Text] [Related]
63. An evaluation of beta-turn prediction methods.
Kaur H; Raghava GP
Bioinformatics; 2002 Nov; 18(11):1508-14. PubMed ID: 12424123
[TBL] [Abstract][Full Text] [Related]
64. Application of multiple sequence alignment profiles to improve protein secondary structure prediction.
Cuff JA; Barton GJ
Proteins; 2000 Aug; 40(3):502-11. PubMed ID: 10861942
[TBL] [Abstract][Full Text] [Related]
65. PHD--an automatic mail server for protein secondary structure prediction.
Rost B; Sander C; Schneider R
Comput Appl Biosci; 1994 Feb; 10(1):53-60. PubMed ID: 8193956
[TBL] [Abstract][Full Text] [Related]
66. Knowledge base and neural network approach for protein secondary structure prediction.
Patel MS; Mazumdar HS
J Theor Biol; 2014 Nov; 361():182-9. PubMed ID: 25128736
[TBL] [Abstract][Full Text] [Related]
67. Prediction of protein secondary structure using improved two-level neural network architecture.
Huang X; Huang DS; Zhang GZ; Zhu YP; Li YX
Protein Pept Lett; 2005 Nov; 12(8):805-11. PubMed ID: 16305553
[TBL] [Abstract][Full Text] [Related]
68. BTEVAL: a server for evaluation of beta-turn prediction methods.
Kaur H; Raghava GP
J Bioinform Comput Biol; 2003 Oct; 1(3):495-504. PubMed ID: 15290767
[TBL] [Abstract][Full Text] [Related]
69. BetaTPred: prediction of beta-TURNS in a protein using statistical algorithms.
Kaur H; Raghava GP
Bioinformatics; 2002 Mar; 18(3):498-9. PubMed ID: 11934756
[TBL] [Abstract][Full Text] [Related]
70. Prediction of cis/trans isomerization in proteins using PSI-BLAST profiles and secondary structure information.
Song J; Burrage K; Yuan Z; Huber T
BMC Bioinformatics; 2006 Mar; 7():124. PubMed ID: 16526956
[TBL] [Abstract][Full Text] [Related]
71. In silico platform for predicting and initiating β-turns in a protein at desired locations.
Singh H; Singh S; Raghava GP
Proteins; 2015 May; 83(5):910-21. PubMed ID: 25728793
[TBL] [Abstract][Full Text] [Related]
72. Evaluation and improvement of multiple sequence methods for protein secondary structure prediction.
Cuff JA; Barton GJ
Proteins; 1999 Mar; 34(4):508-19. PubMed ID: 10081963
[TBL] [Abstract][Full Text] [Related]
73. DiANNA: a web server for disulfide connectivity prediction.
Ferrè F; Clote P
Nucleic Acids Res; 2005 Jul; 33(Web Server issue):W230-2. PubMed ID: 15980459
[TBL] [Abstract][Full Text] [Related]
74. Motif-based fold assignment.
Salwinski L; Eisenberg D
Protein Sci; 2001 Dec; 10(12):2460-9. PubMed ID: 11714913
[TBL] [Abstract][Full Text] [Related]
75. Prediction of protein folding class using global description of amino acid sequence.
Dubchak I; Muchnik I; Holbrook SR; Kim SH
Proc Natl Acad Sci U S A; 1995 Sep; 92(19):8700-4. PubMed ID: 7568000
[TBL] [Abstract][Full Text] [Related]
76. Using sequence motifs for enhanced neural network prediction of protein distance constraints.
Gorodkin J; Lund O; Andersen CA; Brunak S
Proc Int Conf Intell Syst Mol Biol; 1999; ():95-105. PubMed ID: 10786291
[TBL] [Abstract][Full Text] [Related]
77. Accurate contact predictions using covariation techniques and machine learning.
Kosciolek T; Jones DT
Proteins; 2016 Sep; 84 Suppl 1(Suppl Suppl 1):145-51. PubMed ID: 26205532
[TBL] [Abstract][Full Text] [Related]
78. Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles.
Pollastri G; Przybylski D; Rost B; Baldi P
Proteins; 2002 May; 47(2):228-35. PubMed ID: 11933069
[TBL] [Abstract][Full Text] [Related]
79. Improved performance in protein secondary structure prediction by inhomogeneous score combination.
Guermeur Y; Geourjon C; Gallinari P; Deléage G
Bioinformatics; 1999 May; 15(5):413-21. PubMed ID: 10366661
[TBL] [Abstract][Full Text] [Related]
80. Prediction of contact maps with neural networks and correlated mutations.
Fariselli P; Olmea O; Valencia A; Casadio R
Protein Eng; 2001 Nov; 14(11):835-43. PubMed ID: 11742102
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
