BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

122 related articles for article (PubMed ID: 15281121)

  • 21. Prediction of protein secondary structure by neural networks: encoding short and long range patterns of amino acid packing.
    Vieth M; KoliƄski A; Skolnick J; Sikorski A
    Acta Biochim Pol; 1992; 39(4):369-92. PubMed ID: 1293893
    [TBL] [Abstract][Full Text] [Related]  

  • 22. Improved sequence-based prediction of protein secondary structures by combining vacuum-ultraviolet circular dichroism spectroscopy with neural network.
    Matsuo K; Watanabe H; Gekko K
    Proteins; 2008 Oct; 73(1):104-12. PubMed ID: 18395813
    [TBL] [Abstract][Full Text] [Related]  

  • 23. Progress of 1D protein structure prediction at last.
    Rost B; Sander C
    Proteins; 1995 Nov; 23(3):295-300. PubMed ID: 8710823
    [TBL] [Abstract][Full Text] [Related]  

  • 24. Prediction of protein tertiary structure using PROFESY, a novel method based on fragment assembly and conformational space annealing.
    Lee J; Kim SY; Joo K; Kim I; Lee J
    Proteins; 2004 Sep; 56(4):704-14. PubMed ID: 15281124
    [TBL] [Abstract][Full Text] [Related]  

  • 25. Protein secondary structure prediction with partially recurrent neural networks.
    Reczko M
    SAR QSAR Environ Res; 1993; 1(2-3):153-9. PubMed ID: 8790631
    [TBL] [Abstract][Full Text] [Related]  

  • 26. Role of evolutionary information in predicting the disulfide-bonding state of cysteine in proteins.
    Fariselli P; Riccobelli P; Casadio R
    Proteins; 1999 Aug; 36(3):340-6. PubMed ID: 10409827
    [TBL] [Abstract][Full Text] [Related]  

  • 27. Prediction of turn types in protein structure by machine-learning classifiers.
    Meissner M; Koch O; Klebe G; Schneider G
    Proteins; 2009 Feb; 74(2):344-52. PubMed ID: 18618702
    [TBL] [Abstract][Full Text] [Related]  

  • 28. 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. PubMed ID: 15706504
    [TBL] [Abstract][Full Text] [Related]  

  • 29. Modeling protein loops with knowledge-based prediction of sequence-structure alignment.
    Peng HP; Yang AS
    Bioinformatics; 2007 Nov; 23(21):2836-42. PubMed ID: 17827204
    [TBL] [Abstract][Full Text] [Related]  

  • 30. Neural network-based prediction of mutation-induced protein stability changes in Staphylococcal nuclease at 20 residue positions.
    Frenz CM
    Proteins; 2005 May; 59(2):147-51. PubMed ID: 15723345
    [TBL] [Abstract][Full Text] [Related]  

  • 31. Combining prediction of secondary structure and solvent accessibility in proteins.
    Adamczak R; Porollo A; Meller J
    Proteins; 2005 May; 59(3):467-75. PubMed ID: 15768403
    [TBL] [Abstract][Full Text] [Related]  

  • 32. Protein secondary structure prediction using three neural networks and a segmental semi Markov model.
    Malekpour SA; Naghizadeh S; Pezeshk H; Sadeghi M; Eslahchi C
    Math Biosci; 2009 Feb; 217(2):145-50. PubMed ID: 19046975
    [TBL] [Abstract][Full Text] [Related]  

  • 33. High accuracy prediction of beta-turns and their types using propensities and multiple alignments.
    Fuchs PF; Alix AJ
    Proteins; 2005 Jun; 59(4):828-39. PubMed ID: 15822097
    [TBL] [Abstract][Full Text] [Related]  

  • 34. Prediction of helix-helix contacts and interacting helices in polytopic membrane proteins using neural networks.
    Fuchs A; Kirschner A; Frishman D
    Proteins; 2009 Mar; 74(4):857-71. PubMed ID: 18704938
    [TBL] [Abstract][Full Text] [Related]  

  • 35. PPRODO: prediction of protein domain boundaries using neural networks.
    Sim J; Kim SY; Lee J
    Proteins; 2005 May; 59(3):627-32. PubMed ID: 15789433
    [TBL] [Abstract][Full Text] [Related]  

  • 36. Prediction of disordered regions in proteins from position specific score matrices.
    Jones DT; Ward JJ
    Proteins; 2003; 53 Suppl 6():573-8. PubMed ID: 14579348
    [TBL] [Abstract][Full Text] [Related]  

  • 37. A statistical approach using network structure in the prediction of protein characteristics.
    Chen PY; Deane CM; Reinert G
    Bioinformatics; 2007 Sep; 23(17):2314-21. PubMed ID: 17599931
    [TBL] [Abstract][Full Text] [Related]  

  • 38. Achieving 80% ten-fold cross-validated accuracy for secondary structure prediction by large-scale training.
    Dor O; Zhou Y
    Proteins; 2007 Mar; 66(4):838-45. PubMed ID: 17177203
    [TBL] [Abstract][Full Text] [Related]  

  • 39. Prediction of protein secondary structure at 80% accuracy.
    Petersen TN; Lundegaard C; Nielsen M; Bohr H; Bohr J; Brunak S; Gippert GP; Lund O
    Proteins; 2000 Oct; 41(1):17-20. PubMed ID: 10944389
    [TBL] [Abstract][Full Text] [Related]  

  • 40. Improved prediction for N-termini of alpha-helices using empirical information.
    Wilson CL; Boardman PE; Doig AJ; Hubbard SJ
    Proteins; 2004 Nov; 57(2):322-30. PubMed ID: 15340919
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 7.