286 related articles for article (PubMed ID: 24068907)
1. Target inhibition networks: predicting selective combinations of druggable targets to block cancer survival pathways.
Tang J; Karhinen L; Xu T; Szwajda A; Yadav B; Wennerberg K; Aittokallio T
PLoS Comput Biol; 2013; 9(9):e1003226. PubMed ID: 24068907
[TBL] [Abstract][Full Text] [Related]
2. Validation of a network-based strategy for the optimization of combinatorial target selection in breast cancer therapy: siRNA knockdown of network targets in MDA-MB-231 cells as an in vitro model for inhibition of tumor development.
Tilli TM; Carels N; Tuszynski JA; Pasdar M
Oncotarget; 2016 Sep; 7(39):63189-63203. PubMed ID: 27527857
[TBL] [Abstract][Full Text] [Related]
3. Network pharmacology strategies toward multi-target anticancer therapies: from computational models to experimental design principles.
Tang J; Aittokallio T
Curr Pharm Des; 2014; 20(1):23-36. PubMed ID: 23530504
[TBL] [Abstract][Full Text] [Related]
4. TIMMA-R: an R package for predicting synergistic multi-targeted drug combinations in cancer cell lines or patient-derived samples.
He L; Wennerberg K; Aittokallio T; Tang J
Bioinformatics; 2015 Jun; 31(11):1866-8. PubMed ID: 25638808
[TBL] [Abstract][Full Text] [Related]
5. Network pharmacology modeling identifies synergistic Aurora B and ZAK interaction in triple-negative breast cancer.
Tang J; Gautam P; Gupta A; He L; Timonen S; Akimov Y; Wang W; Szwajda A; Jaiswal A; Turei D; Yadav B; Kankainen M; Saarela J; Saez-Rodriguez J; Wennerberg K; Aittokallio T
NPJ Syst Biol Appl; 2019; 5():20. PubMed ID: 31312514
[TBL] [Abstract][Full Text] [Related]
6. SCNrank: spectral clustering for network-based ranking to reveal potential drug targets and its application in pancreatic ductal adenocarcinoma.
Liu E; Zhang ZZ; Cheng X; Liu X; Cheng L
BMC Med Genomics; 2020 Apr; 13(Suppl 5):50. PubMed ID: 32241274
[TBL] [Abstract][Full Text] [Related]
7. Screening drug target combinations in disease-related molecular networks.
Luo M; Jiao J; Wang R
BMC Bioinformatics; 2019 May; 20(Suppl 7):198. PubMed ID: 31074386
[TBL] [Abstract][Full Text] [Related]
8. Driver network as a biomarker: systematic integration and network modeling of multi-omics data to derive driver signaling pathways for drug combination prediction.
Huang L; Brunell D; Stephan C; Mancuso J; Yu X; He B; Thompson TC; Zinner R; Kim J; Davies P; Wong STC
Bioinformatics; 2019 Oct; 35(19):3709-3717. PubMed ID: 30768150
[TBL] [Abstract][Full Text] [Related]
9. Patient-Customized Drug Combination Prediction and Testing for T-cell Prolymphocytic Leukemia Patients.
He L; Tang J; Andersson EI; Timonen S; Koschmieder S; Wennerberg K; Mustjoki S; Aittokallio T
Cancer Res; 2018 May; 78(9):2407-2418. PubMed ID: 29483097
[TBL] [Abstract][Full Text] [Related]
10. Development and validation of a general approach to predict and quantify the synergism of anti-cancer drugs using experimental design and artificial neural networks.
Pivetta T; Isaia F; Trudu F; Pani A; Manca M; Perra D; Amato F; Havel J
Talanta; 2013 Oct; 115():84-93. PubMed ID: 24054565
[TBL] [Abstract][Full Text] [Related]
11. Pharmacological and small interference RNA-mediated inhibition of breast cancer-associated fatty acid synthase (oncogenic antigen-519) synergistically enhances Taxol (paclitaxel)-induced cytotoxicity.
Menendez JA; Vellon L; Colomer R; Lupu R
Int J Cancer; 2005 May; 115(1):19-35. PubMed ID: 15657900
[TBL] [Abstract][Full Text] [Related]
12. Individualized genetic network analysis reveals new therapeutic vulnerabilities in 6,700 cancer genomes.
Liu C; Zhao J; Lu W; Dai Y; Hockings J; Zhou Y; Nussinov R; Eng C; Cheng F
PLoS Comput Biol; 2020 Feb; 16(2):e1007701. PubMed ID: 32101536
[TBL] [Abstract][Full Text] [Related]
13. The future of Cochrane Neonatal.
Soll RF; Ovelman C; McGuire W
Early Hum Dev; 2020 Nov; 150():105191. PubMed ID: 33036834
[TBL] [Abstract][Full Text] [Related]
14. Multi-targeted kinase inhibition alleviates mTOR inhibitor resistance in triple-negative breast cancer.
He J; McLaughlin RP; van der Noord V; Foekens JA; Martens JWM; van Westen G; Zhang Y; van de Water B
Breast Cancer Res Treat; 2019 Nov; 178(2):263-274. PubMed ID: 31388935
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. Improvement of conventional anti-cancer drugs as new tools against multidrug resistant tumors.
Dallavalle S; Dobričić V; Lazzarato L; Gazzano E; Machuqueiro M; Pajeva I; Tsakovska I; Zidar N; Fruttero R
Drug Resist Updat; 2020 May; 50():100682. PubMed ID: 32087558
[TBL] [Abstract][Full Text] [Related]
17. A method for predicting target drug efficiency in cancer based on the analysis of signaling pathway activation.
Artemov A; Aliper A; Korzinkin M; Lezhnina K; Jellen L; Zhukov N; Roumiantsev S; Gaifullin N; Zhavoronkov A; Borisov N; Buzdin A
Oncotarget; 2015 Oct; 6(30):29347-56. PubMed ID: 26320181
[TBL] [Abstract][Full Text] [Related]
18. Combining genomic and network characteristics for extended capability in predicting synergistic drugs for cancer.
Sun Y; Sheng Z; Ma C; Tang K; Zhu R; Wu Z; Shen R; Feng J; Wu D; Huang D; Huang D; Fei J; Liu Q; Cao Z
Nat Commun; 2015 Sep; 6():8481. PubMed ID: 26412466
[TBL] [Abstract][Full Text] [Related]
19. Optimizing drug combination and mechanism analysis based on risk pathway crosstalk in pan cancer.
Hu C; Mi W; Li F; Zhu L; Ou Q; Li M; Li T; Ma Y; Zhang Y; Xu Y
Sci Data; 2024 Jan; 11(1):74. PubMed ID: 38228620
[TBL] [Abstract][Full Text] [Related]
20. 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]
[Next] [New Search]
