These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

99 related articles for article (PubMed ID: 8481816)

  • 21. Context-based features enhance protein secondary structure prediction accuracy.
    Yaseen A; Li Y
    J Chem Inf Model; 2014 Mar; 54(3):992-1002. PubMed ID: 24571803
    [TBL] [Abstract][Full Text] [Related]  

  • 22. Prediction of protein supersecondary structures based on the artificial neural network method.
    Sun Z; Rao X; Peng L; Xu D
    Protein Eng; 1997 Jul; 10(7):763-9. PubMed ID: 9342142
    [TBL] [Abstract][Full Text] [Related]  

  • 23. Prediction of beta-turns at over 80% accuracy based on an ensemble of predicted secondary structures and multiple alignments.
    Zheng C; Kurgan L
    BMC Bioinformatics; 2008 Oct; 9():430. PubMed ID: 18847492
    [TBL] [Abstract][Full Text] [Related]  

  • 24. Limits on alpha-helix prediction with neural network models.
    Hayward S; Collins JF
    Proteins; 1992 Nov; 14(3):372-81. PubMed ID: 1438176
    [TBL] [Abstract][Full Text] [Related]  

  • 25. Predicting secondary structures of membrane proteins with neural networks.
    Fariselli P; Compiani M; Casadio R
    Eur Biophys J; 1993; 22(1):41-51. PubMed ID: 8513752
    [TBL] [Abstract][Full Text] [Related]  

  • 26. A neural network model for the prediction of membrane-spanning amino acid sequences.
    Lohmann R; Schneider G; Behrens D; Wrede P
    Protein Sci; 1994 Sep; 3(9):1597-601. PubMed ID: 7833818
    [TBL] [Abstract][Full Text] [Related]  

  • 27. Sequence representation and prediction of protein secondary structure for structural motifs in twilight zone proteins.
    Kurgan L; Kedarisetti KD
    Protein J; 2006 Dec; 25(7-8):463-74. PubMed ID: 17115254
    [TBL] [Abstract][Full Text] [Related]  

  • 28. Prediction of the location and type of beta-turns in proteins using neural networks.
    Shepherd AJ; Gorse D; Thornton JM
    Protein Sci; 1999 May; 8(5):1045-55. PubMed ID: 10338015
    [TBL] [Abstract][Full Text] [Related]  

  • 29. Relationships between secondary structure fractions for globular proteins. Neural network analyses of crystallographic data sets.
    Pancoska P; Blazek M; Keiderling TA
    Biochemistry; 1992 Oct; 31(42):10250-7. PubMed ID: 1420145
    [TBL] [Abstract][Full Text] [Related]  

  • 30. MUFOLD-SS: New deep inception-inside-inception networks for protein secondary structure prediction.
    Fang C; Shang Y; Xu D
    Proteins; 2018 May; 86(5):592-598. PubMed ID: 29492997
    [TBL] [Abstract][Full Text] [Related]  

  • 31. 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]  

  • 32. Improved prediction of protein secondary structure by use of sequence profiles and neural networks.
    Rost B; Sander C
    Proc Natl Acad Sci U S A; 1993 Aug; 90(16):7558-62. PubMed ID: 8356056
    [TBL] [Abstract][Full Text] [Related]  

  • 33. Prediction of protein secondary structure from circular dichroism spectra using artificial neural network techniques.
    Dalmas B; Hunter GJ; Bannister WH
    Biochem Mol Biol Int; 1994 Aug; 34(1):17-26. PubMed ID: 7849619
    [TBL] [Abstract][Full Text] [Related]  

  • 34. Prediction of protein secondary structure using probability based features and a hybrid system.
    Ghanty P; Pal NR; Mudi RK
    J Bioinform Comput Biol; 2013 Oct; 11(5):1350012. PubMed ID: 24131056
    [TBL] [Abstract][Full Text] [Related]  

  • 35. 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]  

  • 36. 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]  

  • 37. Peptides secondary structure prediction with neural networks: a criterion for building appropriate learning sets.
    Ruggiero C; Sacile R; Rauch G
    IEEE Trans Biomed Eng; 1993 Nov; 40(11):1114-21. PubMed ID: 8307594
    [TBL] [Abstract][Full Text] [Related]  

  • 38. Neural networks predict protein structure and function.
    Punta M; Rost B
    Methods Mol Biol; 2008; 458():203-30. PubMed ID: 19065812
    [TBL] [Abstract][Full Text] [Related]  

  • 39. Protein secondary structure prediction using nearest-neighbor methods.
    Yi TM; Lander ES
    J Mol Biol; 1993 Aug; 232(4):1117-29. PubMed ID: 8371270
    [TBL] [Abstract][Full Text] [Related]  

  • 40. The importance of larger data sets for protein secondary structure prediction with neural networks.
    Chandonia JM; Karplus M
    Protein Sci; 1996 Apr; 5(4):768-74. PubMed ID: 8845767
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 5.