BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

146 related articles for article (PubMed ID: 37094465)

  • 1. Prediction of drug-target interactions via neural tangent kernel extraction feature matrix factorization model.
    Wang Y; Zhang Y; Wang J; Xie F; Zheng D; Zou X; Guo M; Ding Y; Wan J; Han K
    Comput Biol Med; 2023 Jun; 159():106955. PubMed ID: 37094465
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Graph regularized non-negative matrix factorization with prior knowledge consistency constraint for drug-target interactions prediction.
    Zhang J; Xie M
    BMC Bioinformatics; 2022 Dec; 23(1):564. PubMed ID: 36581822
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Identification of drug-target interactions via multiple kernel-based triple collaborative matrix factorization.
    Ding Y; Tang J; Guo F; Zou Q
    Brief Bioinform; 2022 Mar; 23(2):. PubMed ID: 35134117
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Graph regularized non-negative matrix factorization with [Formula: see text] norm regularization terms for drug-target interactions prediction.
    Zhang J; Xie M
    BMC Bioinformatics; 2023 Oct; 24(1):375. PubMed ID: 37789278
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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
    [TBL] [Abstract][Full Text] [Related]  

  • 6. NMTF-DTI: A Nonnegative Matrix Tri-factorization Approach With Multiple Kernel Fusion for Drug-Target Interaction Prediction.
    Jamali AA; Kusalik A; Wu FX
    IEEE/ACM Trans Comput Biol Bioinform; 2023; 20(1):586-594. PubMed ID: 34914594
    [TBL] [Abstract][Full Text] [Related]  

  • 7. DRaW: prediction of COVID-19 antivirals by deep learning-an objection on using matrix factorization.
    Hashemi SM; Zabihian A; Hooshmand M; Gharaghani S
    BMC Bioinformatics; 2023 Feb; 24(1):52. PubMed ID: 36793010
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Integrative approach for predicting drug-target interactions via matrix factorization and broad learning systems.
    Xu W; Yang X; Guan Y; Cheng X; Wang Y
    Math Biosci Eng; 2024 Jan; 21(2):2608-2625. PubMed ID: 38454698
    [TBL] [Abstract][Full Text] [Related]  

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

  • 10. SMGCN: Multiple Similarity and Multiple Kernel Fusion Based Graph Convolutional Neural Network for Drug-Target Interactions Prediction.
    Wang W; Yu M; Sun B; Li J; Liu D; Zhang H; Wang X; Zhou Y
    IEEE/ACM Trans Comput Biol Bioinform; 2024; 21(1):143-154. PubMed ID: 38051618
    [TBL] [Abstract][Full Text] [Related]  

  • 11. A Method of Optimizing Weight Allocation in Data Integration Based on Q-Learning for Drug-Target Interaction Prediction.
    Sun J; Lu Y; Cui L; Fu Q; Wu H; Chen J
    Front Cell Dev Biol; 2022; 10():794413. PubMed ID: 35356288
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Identification of drug-side effect association via correntropy-loss based matrix factorization with neural tangent kernel.
    Ding Y; Zhou H; Zou Q; Yuan L
    Methods; 2023 Nov; 219():73-81. PubMed ID: 37783242
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Multiple similarity drug-target interaction prediction with random walks and matrix factorization.
    Liu B; Papadopoulos D; Malliaros FD; Tsoumakas G; Papadopoulos AN
    Brief Bioinform; 2022 Sep; 23(5):. PubMed ID: 36070659
    [TBL] [Abstract][Full Text] [Related]  

  • 14. EDC-DTI: An end-to-end deep collaborative learning model based on multiple information for drug-target interactions prediction.
    Yuan Y; Zhang Y; Meng X; Liu Z; Wang B; Miao R; Zhang R; Su W; Liu L
    J Mol Graph Model; 2023 Jul; 122():108498. PubMed ID: 37126908
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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; 23(3):. PubMed ID: 35262678
    [TBL] [Abstract][Full Text] [Related]  

  • 16. IMCHGAN: Inductive Matrix Completion With Heterogeneous Graph Attention Networks for Drug-Target Interactions Prediction.
    Li J; Wang J; Lv H; Zhang Z; Wang Z
    IEEE/ACM Trans Comput Biol Bioinform; 2022; 19(2):655-665. PubMed ID: 34115592
    [TBL] [Abstract][Full Text] [Related]  

  • 17. De Novo Prediction of Drug-Target Interactions Using Laplacian Regularized Schatten
    Wu G; Yang M; Li Y; Wang J
    J Comput Biol; 2021 Jul; 28(7):660-673. PubMed ID: 33481664
    [No Abstract]   [Full Text] [Related]  

  • 18. Drug-Target Interaction Prediction via Dual Laplacian Graph Regularized Logistic Matrix Factorization.
    Wang A; Wang M
    Biomed Res Int; 2021; 2021():5599263. PubMed ID: 33855072
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Matrix factorization with denoising autoencoders for prediction of drug-target interactions.
    Sajadi SZ; Zare Chahooki MA; Tavakol M; Gharaghani S
    Mol Divers; 2023 Jun; 27(3):1333-1343. PubMed ID: 35871213
    [TBL] [Abstract][Full Text] [Related]  

  • 20. 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; 204():269-277. PubMed ID: 35219861
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.