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 *

249 related articles for article (PubMed ID: 24015221)

  • 1. iGPCR-drug: a web server for predicting interaction between GPCRs and drugs in cellular networking.
    Xiao X; Min JL; Wang P; Chou KC
    PLoS One; 2013; 8(8):e72234. PubMed ID: 24015221
    [TBL] [Abstract][Full Text] [Related]  

  • 2. iCDI-PseFpt: identify the channel-drug interaction in cellular networking with PseAAC and molecular fingerprints.
    Xiao X; Min JL; Wang P; Chou KC
    J Theor Biol; 2013 Nov; 337():71-9. PubMed ID: 23988798
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Predict drug-protein interaction in cellular networking.
    Xiao X; Min JL; Wang P; Chou KC
    Curr Top Med Chem; 2013; 13(14):1707-12. PubMed ID: 23889048
    [TBL] [Abstract][Full Text] [Related]  

  • 4. iEzy-drug: a web server for identifying the interaction between enzymes and drugs in cellular networking.
    Min JL; Xiao X; Chou KC
    Biomed Res Int; 2013; 2013():701317. PubMed ID: 24371828
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Predicting secretory proteins of malaria parasite by incorporating sequence evolution information into pseudo amino acid composition via grey system model.
    Lin WZ; Fang JA; Xiao X; Chou KC
    PLoS One; 2012; 7(11):e49040. PubMed ID: 23189138
    [TBL] [Abstract][Full Text] [Related]  

  • 6. GPCR-2L: predicting G protein-coupled receptors and their types by hybridizing two different modes of pseudo amino acid compositions.
    Xiao X; Wang P; Chou KC
    Mol Biosyst; 2011 Mar; 7(3):911-9. PubMed ID: 21180772
    [TBL] [Abstract][Full Text] [Related]  

  • 7. iDrug-Target: predicting the interactions between drug compounds and target proteins in cellular networking via benchmark dataset optimization approach.
    Xiao X; Min JL; Lin WZ; Liu Z; Cheng X; Chou KC
    J Biomol Struct Dyn; 2015; 33(10):2221-33. PubMed ID: 25513722
    [TBL] [Abstract][Full Text] [Related]  

  • 8. iNR-Drug: predicting the interaction of drugs with nuclear receptors in cellular networking.
    Fan YN; Xiao X; Min JL; Chou KC
    Int J Mol Sci; 2014 Mar; 15(3):4915-37. PubMed ID: 24651462
    [TBL] [Abstract][Full Text] [Related]  

  • 9. GPCR-GIA: a web-server for identifying G-protein coupled receptors and their families with grey incidence analysis.
    Lin WZ; Xiao X; Chou KC
    Protein Eng Des Sel; 2009 Nov; 22(11):699-705. PubMed ID: 19776029
    [TBL] [Abstract][Full Text] [Related]  

  • 10. iDNA-Prot|dis: identifying DNA-binding proteins by incorporating amino acid distance-pairs and reduced alphabet profile into the general pseudo amino acid composition.
    Liu B; Xu J; Lan X; Xu R; Zhou J; Wang X; Chou KC
    PLoS One; 2014; 9(9):e106691. PubMed ID: 25184541
    [TBL] [Abstract][Full Text] [Related]  

  • 11. iCataly-PseAAC: Identification of Enzymes Catalytic Sites Using Sequence Evolution Information with Grey Model GM (2,1).
    Xiao X; Hui MJ; Liu Z; Qiu WR
    J Membr Biol; 2015 Dec; 248(6):1033-41. PubMed ID: 26077845
    [TBL] [Abstract][Full Text] [Related]  

  • 12. GPCR-CA: A cellular automaton image approach for predicting G-protein-coupled receptor functional classes.
    Xiao X; Wang P; Chou KC
    J Comput Chem; 2009 Jul; 30(9):1414-23. PubMed ID: 19037861
    [TBL] [Abstract][Full Text] [Related]  

  • 13. iNR-PhysChem: a sequence-based predictor for identifying nuclear receptors and their subfamilies via physical-chemical property matrix.
    Xiao X; Wang P; Chou KC
    PLoS One; 2012; 7(2):e30869. PubMed ID: 22363503
    [TBL] [Abstract][Full Text] [Related]  

  • 14. iPPI-PseAAC(CGR): Identify protein-protein interactions by incorporating chaos game representation into PseAAC.
    Jia J; Li X; Qiu W; Xiao X; Chou KC
    J Theor Biol; 2019 Jan; 460():195-203. PubMed ID: 30312687
    [TBL] [Abstract][Full Text] [Related]  

  • 15. iPPI-Esml: An ensemble classifier for identifying the interactions of proteins by incorporating their physicochemical properties and wavelet transforms into PseAAC.
    Jia J; Liu Z; Xiao X; Liu B; Chou KC
    J Theor Biol; 2015 Jul; 377():47-56. PubMed ID: 25908206
    [TBL] [Abstract][Full Text] [Related]  

  • 16. iDNA-Prot: identification of DNA binding proteins using random forest with grey model.
    Lin WZ; Fang JA; Xiao X; Chou KC
    PLoS One; 2011; 6(9):e24756. PubMed ID: 21935457
    [TBL] [Abstract][Full Text] [Related]  

  • 17. GPCR-MPredictor: multi-level prediction of G protein-coupled receptors using genetic ensemble.
    Naveed M; Khan A
    Amino Acids; 2012 May; 42(5):1809-23. PubMed ID: 21505826
    [TBL] [Abstract][Full Text] [Related]  

  • 18. iPhos-PseEvo: Identifying Human Phosphorylated Proteins by Incorporating Evolutionary Information into General PseAAC via Grey System Theory.
    Qiu WR; Sun BQ; Xiao X; Xu D; Chou KC
    Mol Inform; 2017 May; 36(5-6):. PubMed ID: 28488814
    [TBL] [Abstract][Full Text] [Related]  

  • 19. iCDI-W2vCom: Identifying the Ion Channel-Drug Interaction in Cellular Networking Based on word2vec and node2vec.
    Zheng J; Xiao X; Qiu WR
    Front Genet; 2021; 12():738274. PubMed ID: 34567088
    [TBL] [Abstract][Full Text] [Related]  

  • 20. iSNO-PseAAC: predict cysteine S-nitrosylation sites in proteins by incorporating position specific amino acid propensity into pseudo amino acid composition.
    Xu Y; Ding J; Wu LY; Chou KC
    PLoS One; 2013; 8(2):e55844. PubMed ID: 23409062
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 13.