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
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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
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Journal ArticleDOI
Artificial intelligence in cancer target identification and drug discovery
TL;DR: In this paper , the authors describe the scope of artificial intelligence biology analysis for novel anticancer target investigations and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms.
Journal ArticleDOI
Large-Scale Characterization of Drug Responses of Clinically Relevant Proteins in Cancer Cell Lines.
Wei Zhao,Jun Li,Mei-Ju May Chen,Yikai Luo,Yikai Luo,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,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,Han Liang +28 more
TL;DR: This study generated and compiled perturbed expression profiles of clinically relevant proteins in >12,000 cancer cell line samples in response to ∼170 drug compounds using reverse-phase protein arrays and shows that integrating perturbed protein response signals provides mechanistic insights into drug resistance, increases the predictive power for drug sensitivity, and helps identify effective drug combinations.
Journal ArticleDOI
AXL knockdown gene signature reveals a drug repurposing opportunity for a class of antipsychotics to reduce growth and metastasis of triple-negative breast cancer.
Marie-Anne Goyette,Rebecca Cusseddu,Islam E. Elkholi,Afnan Abu-Thuraia,Nehme El-Hachem,Benjamin Haibe-Kains,Jean-Philippe Gratton,Jean-François Côté +7 more
TL;DR: Results suggest that these antipsychotics display anti-tumor and anti-metastatic activity and that they could potentially be repurposed, in combination with standard chemotherapy, for the treatment of TNBC.
Book ChapterDOI
High-Throughput Screening
TL;DR: Current and future trends of how HTS is employed to meet the changing needs for new drug discovery are explored, including the parallel use of complementary screening modalities to sample diverse chemical matter and identify the best starting points for drug discovery programs.
Journal ArticleDOI
In silico drug repositioning: from large-scale transcriptome data to therapeutics
TL;DR: This review briefly outline publicly available large-scale transcriptome databases and tools for drug repositioning, and highlights recent approaches leading to the discovery of novel drug targets, drug response biomarkers, drug indications, and drug MOA.
References
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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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