BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

259 related articles for article (PubMed ID: 34368832)

  • 1. Machine learning approaches for drug combination therapies.
    Güvenç Paltun B; Kaski S; Mamitsuka H
    Brief Bioinform; 2021 Nov; 22(6):. PubMed ID: 34368832
    [TBL] [Abstract][Full Text] [Related]  

  • 2. An integrated framework for identification of effective and synergistic anti-cancer drug combinations.
    Sharma A; Rani R
    J Bioinform Comput Biol; 2018 Oct; 16(5):1850017. PubMed ID: 30304987
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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]  

  • 4. Improving drug response prediction by integrating multiple data sources: matrix factorization, kernel and network-based approaches.
    Güvenç Paltun B; Mamitsuka H; Kaski S
    Brief Bioinform; 2021 Jan; 22(1):346-359. PubMed ID: 31838491
    [TBL] [Abstract][Full Text] [Related]  

  • 5. DeepSynergy: predicting anti-cancer drug synergy with Deep Learning.
    Preuer K; Lewis RPI; Hochreiter S; Bender A; Bulusu KC; Klambauer G
    Bioinformatics; 2018 May; 34(9):1538-1546. PubMed ID: 29253077
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Predictive approaches for drug combination discovery in cancer.
    Madani Tonekaboni SA; Soltan Ghoraie L; Manem VSK; Haibe-Kains B
    Brief Bioinform; 2018 Mar; 19(2):263-276. PubMed ID: 27881431
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Probabilistic modeling of personalized drug combinations from integrated chemical screen and molecular data in sarcoma.
    Berlow NE; Rikhi R; Geltzeiler M; Abraham J; Svalina MN; Davis LE; Wise E; Mancini M; Noujaim J; Mansoor A; Quist MJ; Matlock KL; Goros MW; Hernandez BS; Doung YC; Thway K; Tsukahara T; Nishio J; Huang ET; Airhart S; Bult CJ; Gandour-Edwards R; Maki RG; Jones RL; Michalek JE; Milovancev M; Ghosh S; Pal R; Keller C
    BMC Cancer; 2019 Jun; 19(1):593. PubMed ID: 31208434
    [TBL] [Abstract][Full Text] [Related]  

  • 8. TranSynergy: Mechanism-driven interpretable deep neural network for the synergistic prediction and pathway deconvolution of drug combinations.
    Liu Q; Xie L
    PLoS Comput Biol; 2021 Feb; 17(2):e1008653. PubMed ID: 33577560
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Predicting drug synergy for precision medicine using network biology and machine learning.
    Cuvitoglu A; Zhou JX; Huang S; Isik Z
    J Bioinform Comput Biol; 2019 Apr; 17(2):1950012. PubMed ID: 31057072
    [TBL] [Abstract][Full Text] [Related]  

  • 10. In-silico Prediction of Synergistic Anti-Cancer Drug Combinations Using Multi-omics Data.
    Celebi R; Bear Don't Walk O; Movva R; Alpsoy S; Dumontier M
    Sci Rep; 2019 Jun; 9(1):8949. PubMed ID: 31222109
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Folic acid supplementation and malaria susceptibility and severity among people taking antifolate antimalarial drugs in endemic areas.
    Crider K; Williams J; Qi YP; Gutman J; Yeung L; Mai C; Finkelstain J; Mehta S; Pons-Duran C; Menéndez C; Moraleda C; Rogers L; Daniels K; Green P
    Cochrane Database Syst Rev; 2022 Feb; 2(2022):. PubMed ID: 36321557
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Synergistic Drug Combination Prediction by Integrating Multiomics Data in Deep Learning Models.
    Zhang T; Zhang L; Payne PRO; Li F
    Methods Mol Biol; 2021; 2194():223-238. PubMed ID: 32926369
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A review of machine learning approaches for drug synergy prediction in cancer.
    Torkamannia A; Omidi Y; Ferdousi R
    Brief Bioinform; 2022 May; 23(3):. PubMed ID: 35323854
    [TBL] [Abstract][Full Text] [Related]  

  • 14. A novel approach to predicting the synergy of anti-cancer drug combinations using document-based feature extraction.
    Shim Y; Lee M; Kim PJ; Kim HG
    BMC Bioinformatics; 2022 May; 23(1):163. PubMed ID: 35513784
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Machine learning predicts individual cancer patient responses to therapeutic drugs with high accuracy.
    Huang C; Clayton EA; Matyunina LV; McDonald LD; Benigno BB; Vannberg F; McDonald JF
    Sci Rep; 2018 Nov; 8(1):16444. PubMed ID: 30401894
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Precision Oncology beyond Targeted Therapy: Combining Omics Data with Machine Learning Matches the Majority of Cancer Cells to Effective Therapeutics.
    Ding MQ; Chen L; Cooper GF; Young JD; Lu X
    Mol Cancer Res; 2018 Feb; 16(2):269-278. PubMed ID: 29133589
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Bioinformatics Approaches for Anti-cancer Drug Discovery.
    Li K; Du Y; Li L; Wei DQ
    Curr Drug Targets; 2020; 21(1):3-17. PubMed ID: 31549592
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Designing combination therapies with modeling chaperoned machine learning.
    Zhang Y; Huynh JM; Liu GS; Ballweg R; Aryeh KS; Paek AL; Zhang T
    PLoS Comput Biol; 2019 Sep; 15(9):e1007158. PubMed ID: 31498788
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Bioinformatics Approaches to Predict Drug Responses from Genomic Sequencing.
    Madhukar NS; Elemento O
    Methods Mol Biol; 2018; 1711():277-296. PubMed ID: 29344895
    [TBL] [Abstract][Full Text] [Related]  

  • 20. DeepDRK: a deep learning framework for drug repurposing through kernel-based multi-omics integration.
    Wang Y; Yang Y; Chen S; Wang J
    Brief Bioinform; 2021 Sep; 22(5):. PubMed ID: 33822890
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 13.