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Journal Abstract Search

167 related items for PubMed ID: 26674225

  • 1. GPCR-drug interactions prediction using random forest with drug-association-matrix-based post-processing procedure.
    Hu J, Li Y, Yang JY, Shen HB, Yu DJ.
    Comput Biol Chem; 2016 Feb; 60():59-71. PubMed ID: 26674225
    [Abstract] [Full Text] [Related]

  • 2. 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
    [Abstract] [Full Text] [Related]

  • 3. 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
    [Abstract] [Full Text] [Related]

  • 4. Identification of potential drug-targets by combining evolutionary information extracted from frequency profiles and molecular topological structures.
    Wang L, You ZH, Li LP, Yan X, Zhang W, Song KJ, Song CD.
    Chem Biol Drug Des; 2020 Aug; 96(2):758-767. PubMed ID: 31393672
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  • 5. RFDT: A Rotation Forest-based Predictor for Predicting Drug-Target Interactions Using Drug Structure and Protein Sequence Information.
    Wang L, You ZH, Chen X, Yan X, Liu G, Zhang W.
    Curr Protein Pept Sci; 2018 Aug; 19(5):445-454. PubMed ID: 27842479
    [Abstract] [Full Text] [Related]

  • 6. 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 15; 30(9):1414-23. PubMed ID: 19037861
    [Abstract] [Full Text] [Related]

  • 7. Identifying GPCR-drug interaction based on wordbook learning from sequences.
    Wang P, Huang X, Qiu W, Xiao X.
    BMC Bioinformatics; 2020 Apr 20; 21(1):150. PubMed ID: 32312232
    [Abstract] [Full Text] [Related]

  • 8. Predicting drug-target interactions using Lasso with random forest based on evolutionary information and chemical structure.
    Shi H, Liu S, Chen J, Li X, Ma Q, Yu B.
    Genomics; 2019 Dec 20; 111(6):1839-1852. PubMed ID: 30550813
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  • 13. Disulfide Connectivity Prediction Based on Modelled Protein 3D Structural Information and Random Forest Regression.
    Yu DJ, Li Y, Hu J, Yang X, Yang JY, Shen HB.
    IEEE/ACM Trans Comput Biol Bioinform; 2015 Dec 20; 12(3):611-21. PubMed ID: 26357272
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  • 14. Molecular interaction fingerprint approaches for GPCR drug discovery.
    Vass M, Kooistra AJ, Ritschel T, Leurs R, de Esch IJ, de Graaf C.
    Curr Opin Pharmacol; 2016 Oct 20; 30():59-68. PubMed ID: 27479316
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  • 16. Classifying G protein-coupled receptors and nuclear receptors on the basis of protein power spectrum from fast Fourier transform.
    Guo YZ, Li M, Lu M, Wen Z, Wang K, Li G, Wu J.
    Amino Acids; 2006 Jun 20; 30(4):397-402. PubMed ID: 16773242
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  • 17. 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 Jun 20; 33(10):2221-33. PubMed ID: 25513722
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  • 18. A Systematic Prediction of Drug-Target Interactions Using Molecular Fingerprints and Protein Sequences.
    Huang YA, You ZH, Chen X.
    Curr Protein Pept Sci; 2018 Jun 20; 19(5):468-478. PubMed ID: 27875970
    [Abstract] [Full Text] [Related]

  • 19. GLIDA: GPCR-ligand database for chemical genomic drug discovery.
    Okuno Y, Yang J, Taneishi K, Yabuuchi H, Tsujimoto G.
    Nucleic Acids Res; 2006 Jan 01; 34(Database issue):D673-7. PubMed ID: 16381956
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  • 20. BOW-GBDT: A GBDT Classifier Combining With Artificial Neural Network for Identifying GPCR-Drug Interaction Based on Wordbook Learning From Sequences.
    Qiu W, Lv Z, Hong Y, Jia J, Xiao X.
    Front Cell Dev Biol; 2020 Jan 01; 8():623858. PubMed ID: 33598456
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