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 *

194 related articles for article (PubMed ID: 20689580)

  • 1. Prediction of deleterious non-synonymous SNPs based on protein interaction network and hybrid properties.
    Huang T; Wang P; Ye ZQ; Xu H; He Z; Feng KY; Hu L; Cui W; Wang K; Dong X; Xie L; Kong X; Cai YD; Li Y
    PLoS One; 2010 Jul; 5(7):e11900. PubMed ID: 20689580
    [TBL] [Abstract][Full Text] [Related]  

  • 2. SySAP: a system-level predictor of deleterious single amino acid polymorphisms.
    Huang T; Wang C; Zhang G; Xie L; Li Y
    Protein Cell; 2012 Jan; 3(1):38-43. PubMed ID: 22183811
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Identification of deleterious non-synonymous single nucleotide polymorphisms using sequence-derived information.
    Hu J; Yan C
    BMC Bioinformatics; 2008 Jun; 9():297. PubMed ID: 18588693
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Predicting deleterious non-synonymous single nucleotide polymorphisms in signal peptides based on hybrid sequence attributes.
    Qin W; Li Y; Li J; Yu L; Wu D; Jing R; Pu X; Guo Y; Li M
    Comput Biol Chem; 2012 Feb; 36():31-5. PubMed ID: 22277674
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Discriminating between deleterious and neutral non-frameshifting indels based on protein interaction networks and hybrid properties.
    Zhang N; Huang T; Cai YD
    Mol Genet Genomics; 2015 Feb; 290(1):343-52. PubMed ID: 25248637
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Predicting disease-associated substitution of a single amino acid by analyzing residue interactions.
    Li Y; Wen Z; Xiao J; Yin H; Yu L; Yang L; Li M
    BMC Bioinformatics; 2011 Jan; 12():14. PubMed ID: 21223604
    [TBL] [Abstract][Full Text] [Related]  

  • 7. 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. PubMed ID: 18186470
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Prediction of tyrosine sulfation with mRMR feature selection and analysis.
    Niu S; Huang T; Feng K; Cai Y; Li Y
    J Proteome Res; 2010 Dec; 9(12):6490-7. PubMed ID: 20973568
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Prediction of interactiveness of proteins and nucleic acids based on feature selections.
    Yuan Y; Shi X; Li X; Lu W; Cai Y; Gu L; Liu L; Li M; Kong X; Xing M
    Mol Divers; 2010 Nov; 14(4):627-33. PubMed ID: 19816781
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Combination use of protein-protein interaction network topological features improves the predictive scores of deleterious non-synonymous single-nucleotide polymorphisms.
    Wu Y; Jing R; Jiang L; Jiang Y; Kuang Q; Ye L; Yang L; Li Y; Li M
    Amino Acids; 2014 Aug; 46(8):2025-35. PubMed ID: 24849655
    [TBL] [Abstract][Full Text] [Related]  

  • 11. DAMpred: Recognizing Disease-Associated nsSNPs through Bayes-Guided Neural-Network Model Built on Low-Resolution Structure Prediction of Proteins and Protein-Protein Interactions.
    Quan L; Wu H; Lyu Q; Zhang Y
    J Mol Biol; 2019 Jun; 431(13):2449-2459. PubMed ID: 30796987
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Prediction of protein-protein interaction sites by random forest algorithm with mRMR and IFS.
    Li BQ; Feng KY; Chen L; Huang T; Cai YD
    PLoS One; 2012; 7(8):e43927. PubMed ID: 22937126
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Prediction of the phenotypic effects of non-synonymous single nucleotide polymorphisms using structural and evolutionary information.
    Bao L; Cui Y
    Bioinformatics; 2005 May; 21(10):2185-90. PubMed ID: 15746281
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Accurate Sequence-Based Prediction of Deleterious nsSNPs with Multiple Sequence Profiles and Putative Binding Residues.
    Song R; Cao B; Peng Z; Oldfield CJ; Kurgan L; Wong KC; Yang J
    Biomolecules; 2021 Sep; 11(9):. PubMed ID: 34572550
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Finding new structural and sequence attributes to predict possible disease association of single amino acid polymorphism (SAP).
    Ye ZQ; Zhao SQ; Gao G; Liu XQ; Langlois RE; Lu H; Wei L
    Bioinformatics; 2007 Jun; 23(12):1444-50. PubMed ID: 17384424
    [TBL] [Abstract][Full Text] [Related]  

  • 16. A Feature and Algorithm Selection Method for Improving the Prediction of Protein Structural Class.
    Ni Q; Chen L
    Comb Chem High Throughput Screen; 2017; 20(7):612-621. PubMed ID: 28292249
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Prediction of lysine ubiquitination with mRMR feature selection and analysis.
    Cai Y; Huang T; Hu L; Shi X; Xie L; Li Y
    Amino Acids; 2012 Apr; 42(4):1387-95. PubMed ID: 21267749
    [TBL] [Abstract][Full Text] [Related]  

  • 18. A bioinformatics approach for the phenotype prediction of nonsynonymous single nucleotide polymorphisms in human cytochromes P450.
    Wang LL; Li Y; Zhou SF
    Drug Metab Dispos; 2009 May; 37(5):977-91. PubMed ID: 19204079
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Analysis and Prediction of Myristoylation Sites Using the mRMR Method, the IFS Method and an Extreme Learning Machine Algorithm.
    Wang S; Zhang YH; Huang G; Chen L; Cai YD
    Comb Chem High Throughput Screen; 2017; 20(2):96-106. PubMed ID: 28000567
    [TBL] [Abstract][Full Text] [Related]  

  • 20. PREAL: prediction of allergenic protein by maximum Relevance Minimum Redundancy (mRMR) feature selection.
    Wang J; Zhang D; Li J
    BMC Syst Biol; 2013; 7 Suppl 5(Suppl 5):S9. PubMed ID: 24565053
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.