Signatures of cell death and proliferation in perturbation transcriptomics data—from confounding factor to effective prediction
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Julio Saez-Rodriguez | Bence Szalai | J. Sáez-Rodríguez | L. Puskás | B. Szalai | Vigneshwar Subramanian | R. Alföldi | Vigneshwari Subramanian
[1] Gurmit Singh,et al. Doxycycline and other tetracyclines in the treatment of bone metastasis. , 2003, Anti-cancer drugs.
[2] J. Massagué,et al. Cyclin-dependent Kinase Inhibitors Uncouple Cell Cycle Progression from Mitochondrial Apoptotic Functions in DNA-damaged Cancer Cells* , 2005, Journal of Biological Chemistry.
[3] Paul A Clemons,et al. The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease , 2006, Science.
[4] R. Shoemaker. The NCI60 human tumour cell line anticancer drug screen , 2006, Nature Reviews Cancer.
[5] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[6] Xavier Robin,et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves , 2011, BMC Bioinformatics.
[7] D. di Bernardo,et al. Identification of small molecules enhancing autophagic function from drug network analysis. , 2010, Autophagy.
[8] Tjerk P. Straatsma,et al. NWChem: A comprehensive and scalable open-source solution for large scale molecular simulations , 2010, Comput. Phys. Commun..
[9] Bertram Klinger,et al. Discovering causal signaling pathways through gene-expression patterns , 2010, Nucleic Acids Res..
[10] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[11] S. Ramaswamy,et al. Systematic identification of genomic markers of drug sensitivity in cancer cells , 2012, Nature.
[12] T. Cheng,et al. An Opposite Effect of the CDK Inhibitor, p18INK4c on Embryonic Stem Cells Compared with Tumor and Adult Stem Cells , 2012, PloS one.
[13] Sean R. Davis,et al. NCBI GEO: archive for functional genomics data sets—update , 2012, Nucleic Acids Res..
[14] I. Nookaew,et al. Enriching the gene set analysis of genome-wide data by incorporating directionality of gene expression and combining statistical hypotheses and methods , 2013, Nucleic acids research.
[15] P. McGettigan. Transcriptomics in the RNA-seq era. , 2013, Current opinion in chemical biology.
[16] Benjamin Haibe-Kains,et al. Inconsistency in large pharmacogenomic studies , 2013, Nature.
[17] L. Trojan,et al. Testosterone boosts for treatment of castration resistant prostate cancer: An experimental implementation of intermittent androgen deprivation , 2013, The Prostate.
[18] W. Oh,et al. Roles of Matrix Metalloproteinases and Their Natural Inhibitors in Prostate Cancer Progression , 2014, Cancers.
[19] Nci Dream Community. A community effort to assess and improve drug sensitivity prediction algorithms , 2014 .
[20] L. Wodicka,et al. Dual kinase-bromodomain inhibitors for rationally designed polypharmacology , 2014, Nature chemical biology.
[21] Michael P. Morrissey,et al. Pharmacogenomic agreement between two cancer cell line data sets , 2015, Nature.
[22] Joshua A. Bittker,et al. Harnessing Connectivity in a Large-Scale Small-Molecule Sensitivity Dataset. , 2015, Cancer discovery.
[23] Emanuel J. V. Gonçalves,et al. A Landscape of Pharmacogenomic Interactions in Cancer , 2016, Cell.
[24] M. Kusaka,et al. Growth Inhibition by Testosterone in an Androgen Receptor Splice Variant‐Driven Prostate Cancer Model , 2016, The Prostate.
[25] Y. Hu,et al. ERK5 kinase activity is dispensable for cellular immune response and proliferation , 2016, Proceedings of the National Academy of Sciences.
[26] Kathleen M Jagodnik,et al. Extraction and analysis of signatures from the Gene Expression Omnibus by the crowd , 2016, Nature Communications.
[27] Defining a Cancer Dependency Map , 2017, Cell.
[28] Evan O. Paull,et al. A Community Challenge for Inferring Genetic Predictors of Gene Essentialities through Analysis of a Functional Screen of Cancer Cell Lines. , 2017, Cell systems.
[29] Angela N. Brooks,et al. A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles , 2017, Cell.
[30] Ted Natoli,et al. Evaluation of RNAi and CRISPR technologies by large-scale gene expression profiling in the Connectivity Map , 2017, bioRxiv.
[31] J. Sáez-Rodríguez,et al. Perturbation-response genes reveal signaling footprints in cancer gene expression , 2016, Nature Communications.
[32] Marc Hafner,et al. Common and cell-type specific responses to anti-cancer drugs revealed by high throughput transcript profiling , 2017, Nature Communications.
[33] Jacob K. Asiedu,et al. The Drug Repurposing Hub: a next-generation drug library and information resource , 2017, Nature Medicine.
[34] Tero Aittokallio,et al. Machine learning and feature selection for drug response prediction in precision oncology applications , 2018, Biophysical Reviews.
[35] Nuno A. Fonseca,et al. Transcription Factor Activities Enhance Markers of Drug Sensitivity in Cancer. , 2018, Cancer research.
[36] Rajiv Narayan,et al. The GCTx format and cmap{Py, R, M} packages: resources for the optimized storage and integrated traversal of dense matrices of data and annotations , 2018, bioRxiv.
[37] Doheon Lee,et al. Deconvoluting essential gene signatures for cancer growth from genomic expression in compound-treated cells , 2018, Bioinform..