181 related articles for article (PubMed ID: 26351271)
1. Improved large-scale prediction of growth inhibition patterns using the NCI60 cancer cell line panel.
Cortés-Ciriano I; van Westen GJ; Bouvier G; Nilges M; Overington JP; Bender A; Malliavin TE
Bioinformatics; 2016 Jan; 32(1):85-95. PubMed ID: 26351271
[TBL] [Abstract][Full Text] [Related]
2. Improved anticancer drug response prediction in cell lines using matrix factorization with similarity regularization.
Wang L; Li X; Zhang L; Gao Q
BMC Cancer; 2017 Aug; 17(1):513. PubMed ID: 28768489
[TBL] [Abstract][Full Text] [Related]
3. GDSCTools for mining pharmacogenomic interactions in cancer.
Cokelaer T; Chen E; Iorio F; Menden MP; Lightfoot H; Saez-Rodriguez J; Garnett MJ
Bioinformatics; 2018 Apr; 34(7):1226-1228. PubMed ID: 29186349
[TBL] [Abstract][Full Text] [Related]
4. Current Trends in Drug Sensitivity Prediction.
Cortes-Ciriano I; Mervin LH; Bender A
Curr Pharm Des; 2016; 22(46):6918-6927. PubMed ID: 27784247
[TBL] [Abstract][Full Text] [Related]
5. Orthologue chemical space and its influence on target prediction.
Mervin LH; Bulusu KC; Kalash L; Afzal AM; Svensson F; Firth MA; Barrett I; Engkvist O; Bender A
Bioinformatics; 2018 Jan; 34(1):72-79. PubMed ID: 28961699
[TBL] [Abstract][Full Text] [Related]
6. A novel approach for drug response prediction in cancer cell lines via network representation learning.
Yang J; Li A; Li Y; Guo X; Wang M
Bioinformatics; 2019 May; 35(9):1527-1535. PubMed ID: 30304378
[TBL] [Abstract][Full Text] [Related]
7. Computational probing protein-protein interactions targeting small molecules.
Wang YC; Chen SL; Deng NY; Wang Y
Bioinformatics; 2016 Jan; 32(2):226-34. PubMed ID: 26415726
[TBL] [Abstract][Full Text] [Related]
8. Integrative modeling of multi-omics data to identify cancer drivers and infer patient-specific gene activity.
Pavel AB; Sonkin D; Reddy A
BMC Syst Biol; 2016 Feb; 10():16. PubMed ID: 26864072
[TBL] [Abstract][Full Text] [Related]
9. Modeling gene-wise dependencies improves the identification of drug response biomarkers in cancer studies.
Nikolova O; Moser R; Kemp C; Gönen M; Margolin AA
Bioinformatics; 2017 May; 33(9):1362-1369. PubMed ID: 28082455
[TBL] [Abstract][Full Text] [Related]
10. Molecular analysis of urothelial cancer cell lines for modeling tumor biology and drug response.
Nickerson ML; Witte N; Im KM; Turan S; Owens C; Misner K; Tsang SX; Cai Z; Wu S; Dean M; Costello JC; Theodorescu D
Oncogene; 2017 Jan; 36(1):35-46. PubMed ID: 27270441
[TBL] [Abstract][Full Text] [Related]
11. MACE: mutation-oriented profiling of chemical response and gene expression in cancers.
Jeong E; He N; Park H; Song M; Kim N; Lee S; Yoon S
Bioinformatics; 2015 May; 31(9):1508-14. PubMed ID: 25536965
[TBL] [Abstract][Full Text] [Related]
12. A landscape of synthetic viable interactions in cancer.
Gu Y; Wang R; Han Y; Zhou W; Zhao Z; Chen T; Zhang Y; Peng F; Liang H; Qi L; Zhao W; Yang D; Guo Z
Brief Bioinform; 2018 Jul; 19(4):644-655. PubMed ID: 28096076
[TBL] [Abstract][Full Text] [Related]
13. DeepDSC: A Deep Learning Method to Predict Drug Sensitivity of Cancer Cell Lines.
Li M; Wang Y; Zheng R; Shi X; Li Y; Wu FX; Wang J
IEEE/ACM Trans Comput Biol Bioinform; 2021; 18(2):575-582. PubMed ID: 31150344
[TBL] [Abstract][Full Text] [Related]
14. NCC-AUC: an AUC optimization method to identify multi-biomarker panel for cancer prognosis from genomic and clinical data.
Zou M; Liu Z; Zhang XS; Wang Y
Bioinformatics; 2015 Oct; 31(20):3330-8. PubMed ID: 26092859
[TBL] [Abstract][Full Text] [Related]
15. Accurate prediction of protein-protein interactions by integrating potential evolutionary information embedded in PSSM profile and discriminative vector machine classifier.
Li ZW; You ZH; Chen X; Li LP; Huang DS; Yan GY; Nie R; Huang YA
Oncotarget; 2017 Apr; 8(14):23638-23649. PubMed ID: 28423569
[TBL] [Abstract][Full Text] [Related]
16. Contact map prediction using a large-scale ensemble of rule sets and the fusion of multiple predicted structural features.
Bacardit J; Widera P; Márquez-Chamorro A; Divina F; Aguilar-Ruiz JS; Krasnogor N
Bioinformatics; 2012 Oct; 28(19):2441-8. PubMed ID: 22833524
[TBL] [Abstract][Full Text] [Related]
17. Integration of somatic mutation, expression and functional data reveals potential driver genes predictive of breast cancer survival.
Suo C; Hrydziuszko O; Lee D; Pramana S; Saputra D; Joshi H; Calza S; Pawitan Y
Bioinformatics; 2015 Aug; 31(16):2607-13. PubMed ID: 25810432
[TBL] [Abstract][Full Text] [Related]
18. Characterization of kinase suppressor of Ras-1 expression and anticancer drug sensitivity in human cancer cell lines.
Stoeger SM; Cowan KH
Cancer Chemother Pharmacol; 2009 Apr; 63(5):807-18. PubMed ID: 18661133
[TBL] [Abstract][Full Text] [Related]
19. Induction of senescence in cancer cells by 5'-Aza-2'-deoxycytidine: Bioinformatics and experimental insights to its targets.
Putri JF; Widodo N; Sakamoto K; Kaul SC; Wadhwa R
Comput Biol Chem; 2017 Oct; 70():49-55. PubMed ID: 28802167
[TBL] [Abstract][Full Text] [Related]
20.
; ; . PubMed ID:
[No Abstract] [Full Text] [Related]
[Next] [New Search]
