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.


Pubmed for Handhelds

PUBMED FOR HANDHELDS

Journal Abstract Search

229 related items for PubMed ID: 32312232

  • 1. 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]

  • 2. 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 Apr 20; 8():623858. PubMed ID: 33598456
    [Abstract] [Full Text] [Related]

  • 3. GPCR-MPredictor: multi-level prediction of G protein-coupled receptors using genetic ensemble.
    Naveed M, Khan A.
    Amino Acids; 2012 May 20; 42(5):1809-23. PubMed ID: 21505826
    [Abstract] [Full Text] [Related]

  • 4. GPCR-PEnDB: a database of protein sequences and derived features to facilitate prediction and classification of G protein-coupled receptors.
    Begum K, Mohl JE, Ayivor F, Perez EE, Leung MY.
    Database (Oxford); 2020 Nov 20; 2020():. PubMed ID: 33216895
    [Abstract] [Full Text] [Related]

  • 5. EMCBOW-GPCR: A method for identifying G-protein coupled receptors based on word embedding and wordbooks.
    Qiu W, Lv Z, Xiao X, Shao S, Lin H.
    Comput Struct Biotechnol J; 2021 Nov 20; 19():4961-4969. PubMed ID: 34527200
    [Abstract] [Full Text] [Related]

  • 6. A Machine Learning Approach for Drug-target Interaction Prediction using Wrapper Feature Selection and Class Balancing.
    Redkar S, Mondal S, Joseph A, Hareesha KS.
    Mol Inform; 2020 May 20; 39(5):e1900062. PubMed ID: 32003548
    [Abstract] [Full Text] [Related]

  • 7. Classification of G-protein coupled receptors based on a rich generation of convolutional neural network, N-gram transformation and multiple sequence alignments.
    Li M, Ling C, Xu Q, Gao J.
    Amino Acids; 2018 Feb 20; 50(2):255-266. PubMed ID: 29151135
    [Abstract] [Full Text] [Related]

  • 8. Development and validation of multiple machine learning algorithms for the classification of G-protein-coupled receptors using molecular evolution model-based feature extraction strategy.
    Ling C, Wei X, Shen Y, Zhang H.
    Amino Acids; 2021 Nov 20; 53(11):1705-1714. PubMed ID: 34562175
    [Abstract] [Full Text] [Related]

  • 9. A machine learning model for classifying G-protein-coupled receptors as agonists or antagonists.
    Oh J, Ceong HT, Na D, Park C.
    BMC Bioinformatics; 2022 Aug 18; 23(Suppl 9):346. PubMed ID: 35982407
    [Abstract] [Full Text] [Related]

  • 10. GGIP: Structure and sequence-based GPCR-GPCR interaction pair predictor.
    Nemoto W, Yamanishi Y, Limviphuvadh V, Saito A, Toh H.
    Proteins; 2016 Sep 18; 84(9):1224-33. PubMed ID: 27191053
    [Abstract] [Full Text] [Related]

  • 11. 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 18; 7(3):911-9. PubMed ID: 21180772
    [Abstract] [Full Text] [Related]

  • 12. 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 18; 111(6):1839-1852. PubMed ID: 30550813
    [Abstract] [Full Text] [Related]

  • 13. Prediction of GPCR-Ligand Binding Using Machine Learning Algorithms.
    Seo S, Choi J, Ahn SK, Kim KW, Kim J, Choi J, Kim J, Ahn J.
    Comput Math Methods Med; 2018 Dec 18; 2018():6565241. PubMed ID: 29666662
    [Abstract] [Full Text] [Related]

  • 14. An improved classification of G-protein-coupled receptors using sequence-derived features.
    Peng ZL, Yang JY, Chen X.
    BMC Bioinformatics; 2010 Aug 09; 11():420. PubMed ID: 20696050
    [Abstract] [Full Text] [Related]

  • 15. 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 09; 60():59-71. PubMed ID: 26674225
    [Abstract] [Full Text] [Related]

  • 16. Identifying GPCRs and their types with Chou's pseudo amino acid composition: an approach from multi-scale energy representation and position specific scoring matrix.
    Zia-Ur-Rehman, Khan A.
    Protein Pept Lett; 2012 Aug 09; 19(8):890-903. PubMed ID: 22316312
    [Abstract] [Full Text] [Related]

  • 17. Bioinformatics tools for predicting GPCR gene functions.
    Suwa M.
    Adv Exp Med Biol; 2014 Aug 09; 796():205-24. PubMed ID: 24158807
    [Abstract] [Full Text] [Related]

  • 18. A novel fractal approach for predicting G-protein-coupled receptors and their subfamilies with support vector machines.
    Nie G, Li Y, Wang F, Wang S, Hu X.
    Biomed Mater Eng; 2015 Aug 09; 26 Suppl 1():S1829-36. PubMed ID: 26405954
    [Abstract] [Full Text] [Related]

  • 19. On the hierarchical classification of G protein-coupled receptors.
    Davies MN, Secker A, Freitas AA, Mendao M, Timmis J, Flower DR.
    Bioinformatics; 2007 Dec 01; 23(23):3113-8. PubMed ID: 17956878
    [Abstract] [Full Text] [Related]

  • 20. WDL-RF: predicting bioactivities of ligand molecules acting with G protein-coupled receptors by combining weighted deep learning and random forest.
    Wu J, Zhang Q, Wu W, Pang T, Hu H, Chan WKB, Ke X, Zhang Y.
    Bioinformatics; 2018 Jul 01; 34(13):2271-2282. PubMed ID: 29432522
    [Abstract] [Full Text] [Related]

    Page: [Next] [New Search]
    of 12.