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]