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
More filters
Journal ArticleDOI
Results of the Seventh Scientific Workshop of ECCO: Precision Medicine in IBD-What, Why, and How.
Claudio Fiocchi,Gabriele Dragoni,Dimitrios Iliopoulos,Konstantinos H. Katsanos,Vicent Hernandez Ramirez,Kohei Suzuki,Bram Verstockt,Joana Torres,Michael Scharl +8 more
TL;DR: In this article, the authors address the various complementary aspects of PM in IBD, including what PM is, why it is needed and how it can be used; how PM can contribute to prediction and prevention of IBD; how IBD PM can aid in prognosis and improve response to therapy; and the challenges and future directions of PM.
Posted ContentDOI
Large-scale Characterization of Drug Responses of Clinically Relevant Proteins in Cancer Cell Lines
Wei Zhao,Jun Li,Mei-Ju May Chen,Zhenlin Ju,Nicole K. Nesser,Katie Johnson-Camacho,Christopher Boniface,Yancey Lawrence,Nupur T. Pande,Michael A. Davies,Meenhard Herlyn,Taru A. Muranen,Ioannis K. Zervantonakis,Erika von Euw,Andre Schultz,Shwetha V. Kumar,Anil Korkut,Paul T. Spellman,Rehan Akbani,Dennis J. Slamon,Joe W. Gray,Joan S. Brugge,Yiling Lu,Gordon B. Mills,Han Liang +24 more
TL;DR: It is shown that integrating protein response signals substantially increases the predictive power for drug sensitivity and aids in gaining insights into mechanisms of drug resistance, and a systematic map of protein-drug connectivity is built and developed.
Journal ArticleDOI
6-Phosphogluconate Dehydrogenase Links Cytosolic Carbohydrate Metabolism to Protein Secretion via Modulation of Glutathione Levels.
Haoxin Li,Maria Ericsson,Bokang Rabasha,Bogdan Budnik,Sze Ham Chan,Elizaveta Freinkman,Caroline A. Lewis,John G. Doench,Bridget K. Wagner,Levi A. Garraway,Stuart L. Schreiber,Stuart L. Schreiber +11 more
TL;DR: It is shown that 6-phosphogluconate dehydrogenase (PGD), a cytosolic enzyme involved in carbohydrate metabolism, is required for ER structural integrity and protein secretion and that this characteristic ER-dilation phenotype may be a general marker indicating increased ECM protein congestion inside cells and decreased secretion.
Journal ArticleDOI
Studying metabolic flux adaptations in cancer through integrated experimental-computational approaches.
TL;DR: The applications of these complementary modeling techniques for studying metabolic adaptations in cancer cells due to genetic mutations and the tumor microenvironment, as well as for identifying novel enzymatic targets for anti-cancer drugs are described.
Journal ArticleDOI
Machine learning and network medicine approaches for drug repositioning for COVID-19
TL;DR: In this paper , two machine learning approaches for drug repurposing are presented for SARS-CoV-2 infection/replication in COVID-19 patients, one based on matrix factorization and the other based on network medicine.
References
More filters
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.
Related Papers (5)
The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity
Jordi Barretina,Giordano Caponigro,Nicolas Stransky,Kavitha Venkatesan,Adam A. Margolin,Adam A. Margolin,Sungjoon Kim,Christine D. Wilson,Joseph Lehar,Gregory V. Kryukov,Dmitriy Sonkin,Anupama Reddy,Manway Liu,Lauren Murray,Michael F. Berger,Michael F. Berger,John Monahan,Paula Morais,Jodi Meltzer,Adam Korejwa,Judit Jané-Valbuena,Judit Jané-Valbuena,Felipa A. Mapa,Joseph Thibault,Eva Bric-Furlong,Pichai Raman,Aaron Shipway,Ingo H. Engels,Jill Cheng,Guoying K. Yu,Jianjun Yu,Peter Aspesi,Melanie de Silva,Kalpana Jagtap,Michael D. Jones,Li Wang,Charlie Hatton,Emanuele Palescandolo,Supriya Gupta,Scott Mahan,Carrie Sougnez,Robert C. Onofrio,Ted Liefeld,Laura E. MacConaill,Wendy Winckler,Michael R. Reich,Nanxin Li,Jill P. Mesirov,Stacey Gabriel,Gad Getz,Kristin G. Ardlie,Vivien W. Chan,Vic E. Myer,Barbara L. Weber,Jeffrey A. Porter,Markus Warmuth,Peter Finan,Jennifer L. Harris,Matthew Meyerson,Matthew Meyerson,Todd R. Golub,Michael Morrissey,William R. Sellers,Robert Schlegel,Levi A. Garraway,Levi A. Garraway +65 more