415 related articles for article (PubMed ID: 28583437)
1. Prediction of synergistic anti-cancer drug combinations based on drug target network and drug induced gene expression profiles.
Li X; Xu Y; Cui H; Huang T; Wang D; Lian B; Li W; Qin G; Chen L; Xie L
Artif Intell Med; 2017 Nov; 83():35-43. PubMed ID: 28583437
[TBL] [Abstract][Full Text] [Related]
2. Network Propagation Predicts Drug Synergy in Cancers.
Li H; Li T; Quang D; Guan Y
Cancer Res; 2018 Sep; 78(18):5446-5457. PubMed ID: 30054332
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. SNRMPACDC: computational model focused on Siamese network and random matrix projection for anticancer synergistic drug combination prediction.
Li TH; Wang CC; Zhang L; Chen X
Brief Bioinform; 2023 Jan; 24(1):. PubMed ID: 36418927
[TBL] [Abstract][Full Text] [Related]
5. Comparative analysis of protein interactome networks prioritizes candidate genes with cancer signatures.
Li Y; Sahni N; Yi S
Oncotarget; 2016 Nov; 7(48):78841-78849. PubMed ID: 27791983
[TBL] [Abstract][Full Text] [Related]
6. Diffusion mapping of drug targets on disease signaling network elements reveals drug combination strategies.
Xu J; Regan-Fendt K; Deng S; Carson WE; Payne PRO; Li F
Pac Symp Biocomput; 2018; 23():92-103. PubMed ID: 29218872
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. 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]
9. Synergy from gene expression and network mining (SynGeNet) method predicts synergistic drug combinations for diverse melanoma genomic subtypes.
Regan-Fendt KE; Xu J; DiVincenzo M; Duggan MC; Shakya R; Na R; Carson WE; Payne PRO; Li F
NPJ Syst Biol Appl; 2019; 5():6. PubMed ID: 30820351
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. Systems modelling of the EGFR-PYK2-c-Met interaction network predicts and prioritizes synergistic drug combinations for triple-negative breast cancer.
Shin SY; Müller AK; Verma N; Lev S; Nguyen LK
PLoS Comput Biol; 2018 Jun; 14(6):e1006192. PubMed ID: 29920512
[TBL] [Abstract][Full Text] [Related]
12. Predict effective drug combination by deep belief network and ontology fingerprints.
Chen G; Tsoi A; Xu H; Zheng WJ
J Biomed Inform; 2018 Sep; 85():149-154. PubMed ID: 30081101
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. Optimal control nodes in disease-perturbed networks as targets for combination therapy.
Hu Y; Chen CH; Ding YY; Wen X; Wang B; Gao L; Tan K
Nat Commun; 2019 May; 10(1):2180. PubMed ID: 31097707
[TBL] [Abstract][Full Text] [Related]
15. Drug Repositioning for Cancer Therapy Based on Large-Scale Drug-Induced Transcriptional Signatures.
Lee H; Kang S; Kim W
PLoS One; 2016; 11(3):e0150460. PubMed ID: 26954019
[TBL] [Abstract][Full Text] [Related]
16. A simple gene set-based method accurately predicts the synergy of drug pairs.
Hsu YC; Chiu YC; Chen Y; Hsiao TH; Chuang EY
BMC Syst Biol; 2016 Aug; 10 Suppl 3(Suppl 3):66. PubMed ID: 27585722
[TBL] [Abstract][Full Text] [Related]
17. An enhanced cascade-based deep forest model for drug combination prediction.
Lin W; Wu L; Zhang Y; Wen Y; Yan B; Dai C; Liu K; He S; Bo X
Brief Bioinform; 2022 Mar; 23(2):. PubMed ID: 35062018
[TBL] [Abstract][Full Text] [Related]
18. BMRF-MI: integrative identification of protein interaction network by modeling the gene dependency.
Shi X; Wang X; Shajahan A; Hilakivi-Clarke L; Clarke R; Xuan J
BMC Genomics; 2015; 16 Suppl 7(Suppl 7):S10. PubMed ID: 26099273
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. 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]
[Next] [New Search]
