A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles.
Aravind Subramanian,Rajiv Narayan,Steven M. Corsello,Steven M. Corsello,David Peck,Ted Natoli,Xiaodong Lu,Joshua Gould,John F. Davis,Andrew A. Tubelli,Jacob K. Asiedu,David L. Lahr,Jodi E. Hirschman,Zihan Liu,Melanie Donahue,Bina Julian,Mariya Khan,David Wadden,Ian Smith,Daniel D. Lam,Arthur Liberzon,Courtney Toder,Mukta Bagul,Marek Orzechowski,Oana M. Enache,Federica Piccioni,Sarah A. Johnson,Nicholas J. Lyons,Alice H. Berger,Alice H. Berger,Alykhan F. Shamji,Angela N. Brooks,Angela N. Brooks,Anita Vrcic,Corey Flynn,Jacqueline Rosains,David Y. Takeda,David Y. Takeda,Roger Hu,Desiree Davison,Justin Lamb,Kristin Ardlie,Larson Hogstrom,Peyton Greenside,Nathanael S. Gray,Nathanael S. Gray,Paul A. Clemons,Serena J. Silver,Xiaoyun Wu,Wen-Ning Zhao,Wen-Ning Zhao,Willis Read-Button,Xiaohua Wu,Stephen J. Haggarty,Stephen J. Haggarty,Lucienne Ronco,Jesse S. Boehm,Stuart L. Schreiber,Stuart L. Schreiber,Stuart L. Schreiber,John G. Doench,Joshua A. Bittker,David E. Root,Bang Wong,Todd R. Golub +64 more
TLDR
The expanded CMap is reported, made possible by a new, low-cost, high-throughput reduced representation expression profiling method that is shown to be highly reproducible, comparable to RNA sequencing, and suitable for computational inference of the expression levels of 81% of non-measured transcripts.About:
This article is published in Cell.The article was published on 2017-11-30 and is currently open access. It has received 1943 citations till now.read more
Citations
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DissertationDOI
Precision oncology : oncogenic stat3 signalling in brain tumours
TL;DR: A large number of the patients in this study had at least one parent with a history of central giant cell granuloma, a common complication of Glioblastoma, and the results confirmed the need for further investigation into the mechanisms leading to and effects of this disease.
Journal ArticleDOI
DeepCellState: An autoencoder-based framework for predicting cell type specific transcriptional states induced by drug treatment.
Ramzan Umarov,Yu Li,Erik Arner +2 more
TL;DR: DeepCellState as mentioned in this paper is a deep learning autoencoder-based framework for predicting the induced transcriptional state in a cell type after drug treatment, based on the drug response in another cell type.
Journal ArticleDOI
Predicting mechanism of action of cellular perturbations with pathway activity signatures.
Yan Ren,Siva Sivaganesan,Nicholas A. Clark,Lixia Zhang,Jacek Biesiada,Wen Niu,David R. Plas,Mario Medvedovic +7 more
TL;DR: This work has developed a new computational method for implicating pathway targets of bioactive chemicals and other cellular perturbations by integrated analysis of pathway network topology, the LINCS transcriptional signatures of genetic perturbATIONS of pathway genes and the transcriptional signature of the perturbation.
Journal ArticleDOI
Effects of combined admistration of imatinib and sorafenib in a murine model of liver fibrosis
Antonio Pesce,Rosella Ciurleo,Alessia Bramanti,Eliana Concetta Armeli Iapichino,Maria Cristina Petralia,Gaetano Magro,Paolo Fagone,Placido Bramanti,Ferdinando Nicoletti,Katia Mangano +9 more
TL;DR: Results suggest that treatments with imatinib and sorafenib can modulate potently and, in a superimposable fashion, the fibrinogenic process when administered alone, but only exert partial overlapping antifibrotic effects in modulating the main pathways involved in the process of liver fibrosis.
Journal ArticleDOI
Transcription-based drug repurposing for COVID-19.
TL;DR: The transcriptional response of lung epithelial cells following infection by the original Severe Acute Respiratory Syndrome coronavirus (SARS) is used to identify repositionable drugs for COVID-19 to help elucidate how this seemingly disparate collection of drugs are able to inhibit SARS-2 infection/replication.
References
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Journal ArticleDOI
Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles
Aravind Subramanian,Pablo Tamayo,Vamsi K. Mootha,Sayan Mukherjee,Benjamin L. Ebert,Michael A. Gillette,Amanda G. Paulovich,Scott L. Pomeroy,Todd R. Golub,Eric S. Lander,Jill P. Mesirov +10 more
TL;DR: The Gene Set Enrichment Analysis (GSEA) method as discussed by the authors focuses on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation.
Journal Article
Visualizing Data using t-SNE
TL;DR: A new technique called t-SNE that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map, a variation of Stochastic Neighbor Embedding that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map.
Journal ArticleDOI
Gene Expression Omnibus: NCBI gene expression and hybridization array data repository
TL;DR: The Gene Expression Omnibus (GEO) project was initiated in response to the growing demand for a public repository for high-throughput gene expression data and provides a flexible and open design that facilitates submission, storage and retrieval of heterogeneous data sets from high-power gene expression and genomic hybridization experiments.
Journal ArticleDOI
BLAT—The BLAST-Like Alignment Tool
TL;DR: How BLAT was optimized is described, which is more accurate and 500 times faster than popular existing tools for mRNA/DNA alignments and 50 times faster for protein alignments at sensitivity settings typically used when comparing vertebrate sequences.
Journal ArticleDOI
Adjusting batch effects in microarray expression data using empirical Bayes methods
TL;DR: This paper proposed parametric and non-parametric empirical Bayes frameworks for adjusting data for batch effects that is robust to outliers in small sample sizes and performs comparable to existing methods for large samples.
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