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 *

121 related articles for article (PubMed ID: 37997848)

  • 1. PS4: a next-generation dataset for protein single-sequence secondary structure prediction.
    Peracha O
    Biotechniques; 2024 Feb; 76(2):63-70. PubMed ID: 37997848
    [TBL] [Abstract][Full Text] [Related]  

  • 2. 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]  

  • 3. Protein secondary structure prediction using local alignments.
    Salamov AA; Solovyev VV
    J Mol Biol; 1997 Apr; 268(1):31-6. PubMed ID: 9149139
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Multifaceted analysis of training and testing convolutional neural networks for protein secondary structure prediction.
    Shapovalov M; Dunbrack RL; Vucetic S
    PLoS One; 2020; 15(5):e0232528. PubMed ID: 32374785
    [TBL] [Abstract][Full Text] [Related]  

  • 5. PCI-SS: MISO dynamic nonlinear protein secondary structure prediction.
    Green JR; Korenberg MJ; Aboul-Magd MO
    BMC Bioinformatics; 2009 Jul; 10():222. PubMed ID: 19615046
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Learning sparse models for a dynamic Bayesian network classifier of protein secondary structure.
    Aydin Z; Singh A; Bilmes J; Noble WS
    BMC Bioinformatics; 2011 May; 12():154. PubMed ID: 21569525
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Training set reduction methods for protein secondary structure prediction in single-sequence condition.
    Aydin Z; Altunbasak Y; Pakatci IK; Erdogan H
    Annu Int Conf IEEE Eng Med Biol Soc; 2007; 2007():5025-8. PubMed ID: 18003135
    [TBL] [Abstract][Full Text] [Related]  

  • 8. 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]  

  • 9. YASSPP: better kernels and coding schemes lead to improvements in protein secondary structure prediction.
    Karypis G
    Proteins; 2006 Aug; 64(3):575-86. PubMed ID: 16763996
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Improving protein secondary structure prediction using a multi-modal BP method.
    Qu W; Sui H; Yang B; Qian W
    Comput Biol Med; 2011 Oct; 41(10):946-59. PubMed ID: 21880310
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Protein secondary structure prediction for a single-sequence using hidden semi-Markov models.
    Aydin Z; Altunbasak Y; Borodovsky M
    BMC Bioinformatics; 2006 Mar; 7():178. PubMed ID: 16571137
    [TBL] [Abstract][Full Text] [Related]  

  • 12. The influence of gapped positions in multiple sequence alignments on secondary structure prediction methods.
    Simossis VA; Heringa J
    Comput Biol Chem; 2004 Dec; 28(5-6):351-66. PubMed ID: 15556476
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A simple strategy to enhance the speed of protein secondary structure prediction without sacrificing accuracy.
    Juan SH; Chen TR; Lo WC
    PLoS One; 2020; 15(6):e0235153. PubMed ID: 32603341
    [TBL] [Abstract][Full Text] [Related]  

  • 14. A novel approach for protein secondary structure prediction using encoder-decoder with attention mechanism model.
    Sonsare PM; Gunavathi C
    Biomol Concepts; 2024 Jan; 15(1):. PubMed ID: 38478635
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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]  

  • 16. Protein contact prediction by integrating deep multiple sequence alignments, coevolution and machine learning.
    Adhikari B; Hou J; Cheng J
    Proteins; 2018 Mar; 86 Suppl 1(Suppl 1):84-96. PubMed ID: 29047157
    [TBL] [Abstract][Full Text] [Related]  

  • 17. A neural network method for prediction of beta-turn types in proteins using evolutionary information.
    Kaur H; Raghava GP
    Bioinformatics; 2004 Nov; 20(16):2751-8. PubMed ID: 15145798
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Using predicted shape string to enhance the accuracy of γ-turn prediction.
    Zhu Y; Li T; Li D; Zhang Y; Xiong W; Sun J; Tang Z; Chen G
    Amino Acids; 2012 May; 42(5):1749-55. PubMed ID: 21424809
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Seventy-five percent accuracy in protein secondary structure prediction.
    Frishman D; Argos P
    Proteins; 1997 Mar; 27(3):329-35. PubMed ID: 9094735
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Prediction of protein structural class using novel evolutionary collocation-based sequence representation.
    Chen K; Kurgan LA; Ruan J
    J Comput Chem; 2008 Jul; 29(10):1596-604. PubMed ID: 18293306
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.