BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

144 related articles for article (PubMed ID: 38105262)

  • 1. A multi-task learning model for predicting drugs combination synergy by analyzing drug-drug interactions and integrated multi-view graph data.
    Monem S; Hassanien AE; Abdel-Hamid AH
    Sci Rep; 2023 Dec; 13(1):22463. PubMed ID: 38105262
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Deep learning-based multi-drug synergy prediction model for individually tailored anti-cancer therapies.
    She S; Chen H; Ji W; Sun M; Cheng J; Rui M; Feng C
    Front Pharmacol; 2022; 13():1032875. PubMed ID: 36588694
    [TBL] [Abstract][Full Text] [Related]  

  • 3. PRODeepSyn: predicting anticancer synergistic drug combinations by embedding cell lines with protein-protein interaction network.
    Wang X; Zhu H; Jiang Y; Li Y; Tang C; Chen X; Li Y; Liu Q; Liu Q
    Brief Bioinform; 2022 Mar; 23(2):. PubMed ID: 35043159
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Unlocking the therapeutic potential of drug combinations through synergy prediction using graph transformer networks.
    Alam W; Tayara H; Chong KT
    Comput Biol Med; 2024 Mar; 170():108007. PubMed ID: 38242015
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Predicting anticancer synergistic drug combinations based on multi-task learning.
    Chen D; Wang X; Zhu H; Jiang Y; Li Y; Liu Q; Liu Q
    BMC Bioinformatics; 2023 Nov; 24(1):448. PubMed ID: 38012551
    [TBL] [Abstract][Full Text] [Related]  

  • 6. CFSSynergy: Combining Feature-Based and Similarity-Based Methods for Drug Synergy Prediction.
    Rafiei F; Zeraati H; Abbasi K; Razzaghi P; Ghasemi JB; Parsaeian M; Masoudi-Nejad A
    J Chem Inf Model; 2024 Apr; 64(7):2577-2585. PubMed ID: 38514966
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Predicting drug-drug interactions by graph convolutional network with multi-kernel.
    Wang F; Lei X; Liao B; Wu FX
    Brief Bioinform; 2022 Jan; 23(1):. PubMed ID: 34864856
    [TBL] [Abstract][Full Text] [Related]  

  • 8. MFSynDCP: multi-source feature collaborative interactive learning for drug combination synergy prediction.
    Dong Y; Chang Y; Wang Y; Han Q; Wen X; Yang Z; Zhang Y; Qiang Y; Wu K; Fan X; Ren X
    BMC Bioinformatics; 2024 Apr; 25(1):140. PubMed ID: 38561679
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Drug response prediction using graph representation learning and Laplacian feature selection.
    Xie M; Lei X; Zhong J; Ouyang J; Li G
    BMC Bioinformatics; 2022 Dec; 23(Suppl 8):532. PubMed ID: 36494630
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Deep graph embedding for prioritizing synergistic anticancer drug combinations.
    Jiang P; Huang S; Fu Z; Sun Z; Lakowski TM; Hu P
    Comput Struct Biotechnol J; 2020; 18():427-438. PubMed ID: 32153729
    [TBL] [Abstract][Full Text] [Related]  

  • 11. DEML: Drug Synergy and Interaction Prediction Using Ensemble-Based Multi-Task Learning.
    Wang Z; Dong J; Wu L; Dai C; Wang J; Wen Y; Zhang Y; Yang X; He S; Bo X
    Molecules; 2023 Jan; 28(2):. PubMed ID: 36677903
    [TBL] [Abstract][Full Text] [Related]  

  • 12. DTSyn: a dual-transformer-based neural network to predict synergistic drug combinations.
    Hu J; Gao J; Fang X; Liu Z; Wang F; Huang W; Wu H; Zhao G
    Brief Bioinform; 2022 Sep; 23(5):. PubMed ID: 35915050
    [TBL] [Abstract][Full Text] [Related]  

  • 13. In silico drug combination discovery for personalized cancer therapy.
    Jeon M; Kim S; Park S; Lee H; Kang J
    BMC Syst Biol; 2018 Mar; 12(Suppl 2):16. PubMed ID: 29560824
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Multi-way relation-enhanced hypergraph representation learning for anti-cancer drug synergy prediction.
    Liu X; Song C; Liu S; Li M; Zhou X; Zhang W
    Bioinformatics; 2022 Oct; 38(20):4782-4789. PubMed ID: 36000898
    [TBL] [Abstract][Full Text] [Related]  

  • 15. DeepTraSynergy: drug combinations using multimodal deep learning with transformers.
    Rafiei F; Zeraati H; Abbasi K; Ghasemi JB; Parsaeian M; Masoudi-Nejad A
    Bioinformatics; 2023 Aug; 39(8):. PubMed ID: 37467066
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Prediction of anti-cancer drug synergy based on cross-matching network and cancer molecular subtypes.
    Su R; Han J; Sun C; Zhang D; Geng J; Wang P; Zeng X
    Comput Biol Med; 2024 Jun; 175():108441. PubMed ID: 38663353
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Predicting Drug Synergy and Discovering New Drug Combinations Based on a Graph Autoencoder and Convolutional Neural Network.
    Li H; Zou L; Kowah JAH; He D; Wang L; Yuan M; Liu X
    Interdiscip Sci; 2023 Jun; 15(2):316-330. PubMed ID: 36943614
    [TBL] [Abstract][Full Text] [Related]  

  • 18. SNSynergy: Similarity network-based machine learning framework for synergy prediction towards new cell lines and new anticancer drug combinations.
    Huangfu X; Zhang C; Li H; Li S; Li Y
    Comput Biol Chem; 2024 Jun; 110():108054. PubMed ID: 38522389
    [TBL] [Abstract][Full Text] [Related]  

  • 19. DTF: Deep Tensor Factorization for predicting anticancer drug synergy.
    Sun Z; Huang S; Jiang P; Hu P
    Bioinformatics; 2020 Aug; 36(16):4483-4489. PubMed ID: 32369563
    [TBL] [Abstract][Full Text] [Related]  

  • 20. A complete graph-based approach with multi-task learning for predicting synergistic drug combinations.
    Wang X; Zhu H; Chen D; Yu Y; Liu Q; Liu Q
    Bioinformatics; 2023 Jun; 39(6):. PubMed ID: 37261842
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.