These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
155 related articles for article (PubMed ID: 30864327)
1. AICM: A Genuine Framework for Correcting Inconsistency Between Large Pharmacogenomics Datasets. Hu ZT; Ye Y; Newbury PA; Huang H; Chen B Pac Symp Biocomput; 2019; 24():248-259. PubMed ID: 30864327 [TBL] [Abstract][Full Text] [Related]
2. Systematic assessment of analytical methods for drug sensitivity prediction from cancer cell line data. Jang IS; Neto EC; Guinney J; Friend SH; Margolin AA Pac Symp Biocomput; 2014; ():63-74. PubMed ID: 24297534 [TBL] [Abstract][Full Text] [Related]
3. Large-scale integration of heterogeneous pharmacogenomic data for identifying drug mechanism of action. Luo Y; Wang S; Xiao J; Peng J Pac Symp Biocomput; 2018; 23():44-55. PubMed ID: 29218868 [TBL] [Abstract][Full Text] [Related]
4. 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]
5. Stepwise group sparse regression (SGSR): gene-set-based pharmacogenomic predictive models with stepwise selection of functional priors. Jang IS; Dienstmann R; Margolin AA; Guinney J Pac Symp Biocomput; 2015; 20():32-43. PubMed ID: 25592566 [TBL] [Abstract][Full Text] [Related]
7. Drug Intervention Response Predictions with PARADIGM (DIRPP) identifies drug resistant cancer cell lines and pathway mechanisms of resistance. Brubaker D; Difeo A; Chen Y; Pearl T; Zhai K; Bebek G; Chance M; Barnholtz-Sloan J Pac Symp Biocomput; 2014; ():125-35. PubMed ID: 24297540 [TBL] [Abstract][Full Text] [Related]
8. Comparison and validation of genomic predictors for anticancer drug sensitivity. Papillon-Cavanagh S; De Jay N; Hachem N; Olsen C; Bontempi G; Aerts HJ; Quackenbush J; Haibe-Kains B J Am Med Inform Assoc; 2013; 20(4):597-602. PubMed ID: 23355484 [TBL] [Abstract][Full Text] [Related]
9. Integrating heterogeneous drug sensitivity data from cancer pharmacogenomic studies. Pozdeyev N; Yoo M; Mackie R; Schweppe RE; Tan AC; Haugen BR Oncotarget; 2016 Aug; 7(32):51619-51625. PubMed ID: 27322211 [TBL] [Abstract][Full Text] [Related]
10. Evaluating the consistency of large-scale pharmacogenomic studies. Rahman R; Dhruba SR; Matlock K; De-Niz C; Ghosh S; Pal R Brief Bioinform; 2019 Sep; 20(5):1734-1753. PubMed ID: 31846027 [TBL] [Abstract][Full Text] [Related]
11. CellMiner Cross-Database (CellMinerCDB) version 1.2: Exploration of patient-derived cancer cell line pharmacogenomics. Luna A; Elloumi F; Varma S; Wang Y; Rajapakse VN; Aladjem MI; Robert J; Sander C; Pommier Y; Reinhold WC Nucleic Acids Res; 2021 Jan; 49(D1):D1083-D1093. PubMed ID: 33196823 [TBL] [Abstract][Full Text] [Related]
12. Computational Analyses Connect Small-Molecule Sensitivity to Cellular Features Using Large Panels of Cancer Cell Lines. Rees MG; Seashore-Ludlow B; Clemons PA Methods Mol Biol; 2019; 1888():233-254. PubMed ID: 30519951 [TBL] [Abstract][Full Text] [Related]
13. Large-Scale Information Retrieval and Correction of Noisy Pharmacogenomic Datasets through Residual Thresholded Deep Matrix Factorization. Hu ZT; Yu Y; Chen R; Yeh SJ; Chen B; Huang H bioRxiv; 2023 Dec; ():. PubMed ID: 38106027 [TBL] [Abstract][Full Text] [Related]
14. Pharmacogenomic agreement between two cancer cell line data sets. ; Nature; 2015 Dec; 528(7580):84-7. PubMed ID: 26570998 [TBL] [Abstract][Full Text] [Related]
15. PLATYPUS: A Multiple-View Learning Predictive Framework for Cancer Drug Sensitivity Prediction. Graim K; Friedl V; Houlahan KE; Stuart JM Pac Symp Biocomput; 2019; 24():136-147. PubMed ID: 30864317 [TBL] [Abstract][Full Text] [Related]
16. Link synthetic lethality to drug sensitivity of cancer cells. Wang R; Han Y; Zhao Z; Yang F; Chen T; Zhou W; Wang X; Qi L; Zhao W; Guo Z; Gu Y Brief Bioinform; 2019 Jul; 20(4):1295-1307. PubMed ID: 29300844 [TBL] [Abstract][Full Text] [Related]
17. The Stream algorithm: computationally efficient ridge-regression via Bayesian model averaging, and applications to pharmacogenomic prediction of cancer cell line sensitivity. Neto EC; Jang IS; Friend SH; Margolin AA Pac Symp Biocomput; 2014; ():27-38. PubMed ID: 24297531 [TBL] [Abstract][Full Text] [Related]
18. Drug sensitivity prediction from cell line-based pharmacogenomics data: guidelines for developing machine learning models. Sharifi-Noghabi H; Jahangiri-Tazehkand S; Smirnov P; Hon C; Mammoliti A; Nair SK; Mer AS; Ester M; Haibe-Kains B Brief Bioinform; 2021 Nov; 22(6):. PubMed ID: 34382071 [TBL] [Abstract][Full Text] [Related]
19. Pharmacogenomic data sample collection and storage: ethical issues and policy approaches. Joly Y; Knoppers BM Pharmacogenomics; 2006 Mar; 7(2):219-26. PubMed ID: 16515401 [TBL] [Abstract][Full Text] [Related]
20. Progress towards the integration of pharmacogenomics in practice. Mooney SD Hum Genet; 2015 May; 134(5):459-65. PubMed ID: 25238897 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]