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Journal Abstract Search

230 related items for PubMed ID: 34375902

  • 1. DNN-DTIs: Improved drug-target interactions prediction using XGBoost feature selection and deep neural network.
    Chen C, Shi H, Jiang Z, Salhi A, Chen R, Cui X, Yu B.
    Comput Biol Med; 2021 Sep; 136():104676. PubMed ID: 34375902
    [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; 14(2):311-330. PubMed ID: 34731411
    [Abstract] [Full Text] [Related]

  • 3. Predicting drug-target interaction network using deep learning model.
    You J, McLeod RD, Hu P.
    Comput Biol Chem; 2019 Jun; 80():90-101. PubMed ID: 30939415
    [Abstract] [Full Text] [Related]

  • 4. PreDTIs: prediction of drug-target interactions based on multiple feature information using gradient boosting framework with data balancing and feature selection techniques.
    Mahmud SMH, Chen W, Liu Y, Awal MA, Ahmed K, Rahman MH, Moni MA.
    Brief Bioinform; 2021 Sep 02; 22(5):. PubMed ID: 33709119
    [Abstract] [Full Text] [Related]

  • 5. A deep learning-based method for drug-target interaction prediction based on long short-term memory neural network.
    Wang YB, You ZH, Yang S, Yi HC, Chen ZH, Zheng K.
    BMC Med Inform Decis Mak; 2020 Mar 18; 20(Suppl 2):49. PubMed ID: 32183788
    [Abstract] [Full Text] [Related]

  • 6. The Discovery of New Drug-Target Interactions for Breast Cancer Treatment.
    Song J, Xu Z, Cao L, Wang M, Hou Y, Li K.
    Molecules; 2021 Dec 10; 26(24):. PubMed ID: 34946556
    [Abstract] [Full Text] [Related]

  • 7. 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
    [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
    [Abstract] [Full Text] [Related]

  • 9. 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
    [Abstract] [Full Text] [Related]

  • 10. 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 01; 39(5):e1900062. PubMed ID: 32003548
    [Abstract] [Full Text] [Related]

  • 11. DeepFusion: A deep learning based multi-scale feature fusion method for predicting drug-target interactions.
    Song T, Zhang X, Ding M, Rodriguez-Paton A, Wang S, Wang G.
    Methods; 2022 Aug 01; 204():269-277. PubMed ID: 35219861
    [Abstract] [Full Text] [Related]

  • 12. GSRF-DTI: a framework for drug-target interaction prediction based on a drug-target pair network and representation learning on a large graph.
    Zhu Y, Ning C, Zhang N, Wang M, Zhang Y.
    BMC Biol; 2024 Jul 18; 22(1):156. PubMed ID: 39020316
    [Abstract] [Full Text] [Related]

  • 13. Multiview network embedding for drug-target Interactions prediction by consistent and complementary information preserving.
    Shang Y, Ye X, Futamura Y, Yu L, Sakurai T.
    Brief Bioinform; 2022 May 13; 23(3):. PubMed ID: 35262678
    [Abstract] [Full Text] [Related]

  • 14. Predicting drug-target interactions using restricted Boltzmann machines.
    Wang Y, Zeng J.
    Bioinformatics; 2013 Jul 01; 29(13):i126-34. PubMed ID: 23812976
    [Abstract] [Full Text] [Related]

  • 15. Deep Learning in Drug Target Interaction Prediction: Current and Future Perspectives.
    Abbasi K, Razzaghi P, Poso A, Ghanbari-Ara S, Masoudi-Nejad A.
    Curr Med Chem; 2021 Jul 01; 28(11):2100-2113. PubMed ID: 32895036
    [Abstract] [Full Text] [Related]

  • 16. Improving protein-protein interactions prediction accuracy using XGBoost feature selection and stacked ensemble classifier.
    Chen C, Zhang Q, Yu B, Yu Z, Lawrence PJ, Ma Q, Zhang Y.
    Comput Biol Med; 2020 Aug 01; 123():103899. PubMed ID: 32768046
    [Abstract] [Full Text] [Related]

  • 17. Prediction of protein-protein interaction sites through eXtreme gradient boosting with kernel principal component analysis.
    Wang X, Zhang Y, Yu B, Salhi A, Chen R, Wang L, Liu Z.
    Comput Biol Med; 2021 Jul 01; 134():104516. PubMed ID: 34119922
    [Abstract] [Full Text] [Related]

  • 18. SPVec: A Word2vec-Inspired Feature Representation Method for Drug-Target Interaction Prediction.
    Zhang YF, Wang X, Kaushik AC, Chu Y, Shan X, Zhao MZ, Xu Q, Wei DQ.
    Front Chem; 2019 Jul 01; 7():895. PubMed ID: 31998687
    [Abstract] [Full Text] [Related]

  • 19. NeuRank: learning to rank with neural networks for drug-target interaction prediction.
    Wu X, Zeng W, Lin F, Zhou X.
    BMC Bioinformatics; 2021 Nov 26; 22(1):567. PubMed ID: 34836495
    [Abstract] [Full Text] [Related]

  • 20. DNN-m6A: A Cross-Species Method for Identifying RNA N6-Methyladenosine Sites Based on Deep Neural Network with Multi-Information Fusion.
    Zhang L, Qin X, Liu M, Xu Z, Liu G.
    Genes (Basel); 2021 Feb 28; 12(3):. PubMed ID: 33670877
    [Abstract] [Full Text] [Related]

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