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Journal Abstract Search

142 related items for PubMed ID: 35941686

  • 1. An ensemble-based drug-target interaction prediction approach using multiple feature information with data balancing.
    El-Behery H, Attia AF, El-Fishawy N, Torkey H.
    J Biol Eng; 2022 Aug 08; 16(1):21. PubMed ID: 35941686
    [Abstract] [Full Text] [Related]

  • 2. DeepStack-DTIs: Predicting Drug-Target Interactions Using LightGBM Feature Selection and Deep-Stacked Ensemble Classifier.
    Zhang Y, Jiang Z, Chen C, Wei Q, Gu H, Yu B.
    Interdiscip Sci; 2022 Jun 08; 14(2):311-330. PubMed ID: 34731411
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  • 5. Predicting Drug-Target Interactions Based on the Ensemble Models of Multiple Feature Pairs.
    Wang C, Zhang J, Chen P, Wang B.
    Int J Mol Sci; 2021 Jun 20; 22(12):. PubMed ID: 34202954
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  • 7. Predicting drug-target interaction network using deep learning model.
    You J, McLeod RD, Hu P.
    Comput Biol Chem; 2019 Jun 20; 80():90-101. PubMed ID: 30939415
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  • 8. BE-DTI': Ensemble framework for drug target interaction prediction using dimensionality reduction and active learning.
    Sharma A, Rani R.
    Comput Methods Programs Biomed; 2018 Oct 20; 165():151-162. PubMed ID: 30337070
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  • 9. Predicting drug-target interactions using restricted Boltzmann machines.
    Wang Y, Zeng J.
    Bioinformatics; 2013 Jul 01; 29(13):i126-34. PubMed ID: 23812976
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  • 10. Improving prediction of drug-target interactions based on fusing multiple features with data balancing and feature selection techniques.
    Khojasteh H, Pirgazi J, Ghanbari Sorkhi A.
    PLoS One; 2023 Jul 01; 18(8):e0288173. PubMed ID: 37535616
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  • 12. An efficient computational method for predicting drug-target interactions using weighted extreme learning machine and speed up robot features.
    An JY, Meng FR, Yan ZJ.
    BioData Min; 2021 Jan 20; 14(1):3. PubMed ID: 33472664
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  • 13. SSELM-neg: spherical search-based extreme learning machine for drug-target interaction prediction.
    Hu L, Fu C, Ren Z, Cai Y, Yang J, Xu S, Xu W, Tang D.
    BMC Bioinformatics; 2023 Feb 03; 24(1):38. PubMed ID: 36737694
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  • 14. Identification of human drug targets using machine-learning algorithms.
    Kumari P, Nath A, Chaube R.
    Comput Biol Med; 2015 Jan 03; 56():175-81. PubMed ID: 25437231
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  • 15. DeepACTION: A deep learning-based method for predicting novel drug-target interactions.
    Hasan Mahmud SM, Chen W, Jahan H, Dai B, Din SU, Dzisoo AM.
    Anal Biochem; 2020 Dec 01; 610():113978. PubMed ID: 33035462
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  • 16. Drug-Target Interaction Prediction Based on Drug Fingerprint Information and Protein Sequence.
    Li Y, Huang YA, You ZH, Li LP, Wang Z.
    Molecules; 2019 Aug 19; 24(16):. PubMed ID: 31430892
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  • 17. Ensemble Learning Prediction of Drug-Target Interactions Using GIST Descriptor Extracted from PSSM-Based Evolutionary Information.
    Zhan X, You Z, Yu C, Li L, Pan J.
    Biomed Res Int; 2020 Aug 19; 2020():4516250. PubMed ID: 32908888
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  • 18. A Machine Learning-Based Biological Drug-Target Interaction Prediction Method for a Tripartite Heterogeneous Network.
    Zheng Y, Wu Z.
    ACS Omega; 2021 Feb 02; 6(4):3037-3045. PubMed ID: 33553921
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  • 19. Automated Detection of Driver Fatigue Based on AdaBoost Classifier with EEG Signals.
    Hu J.
    Front Comput Neurosci; 2017 Feb 02; 11():72. PubMed ID: 28824409
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  • 20. Accurate prediction of potential druggable proteins based on genetic algorithm and Bagging-SVM ensemble classifier.
    Lin J, Chen H, Li S, Liu Y, Li X, Yu B.
    Artif Intell Med; 2019 Jul 02; 98():35-47. PubMed ID: 31521251
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