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
Citations
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Journal ArticleDOI
Predicting gastrointestinal drug effects using contextualized metabolic models
TL;DR: It is shown that combining local gut wall-specific metabolism with gene expression performs better than gene expression alone, which indicates the role of small intestine metabolism in the development of adverse reactions.
Journal ArticleDOI
Prioritization of novel ADPKD drug candidates from disease-stage specific gene expression profiles.
Tareq B. Malas,Wouter N. Leonhard,Hester Bange,Zoraide Granchi,Kristina Hettne,Gerard J. P. van Westen,Leo S. Price,Peter A C 't Hoen,Peter A C 't Hoen,Dorien J.M. Peters +9 more
TL;DR: The data establishes drug repurposing as a robust drug discovery method, with three promising drug candidates identified for ADPKD treatment (Meclofenamic Acid, Gamolenic Acid and Birinapant), and can be extended for AD PKD and other diseases in the future.
Journal ArticleDOI
Pterostilbene sensitizes cisplatin-resistant human bladder cancer cells with oncogenic HRAS
Yi Ting Chen,Zi Yi Huang,Han Hsuan Tang,Wan Ting Kuo,Shan Ying Wu,Sheng Hui Lan,Sheng Hui Lan,Kai Hsun Chang,Pin Lun Lin,Ming-Fen Lee,Ming-Fen Lee,Hung Chi Cheng,Hsiao Sheng Liu,Hsiao Sheng Liu,Chi Ying F. Huang,Guan Cheng Huang,Chun Li Su +16 more
TL;DR: Using a novel gene expression screening platform, pterostilbene was identified to sensitize cisplatin-resistant bladder cancer cells with HRAS alterations via RAS-related autophagy and cell senescence pathways, suggesting a potentially chemotherapeutic role of pterstil bene for cisPlatin treatment of human bladder cancer with oncogenic HRAS.
Book ChapterDOI
An Overview of Computational Methods, Tools, Servers, and Databases for Drug Repurposing
TL;DR: The common CADD techniques used for drug design and discovery are discussed, with particular emphasis on the open source in silico tools, databases, and servers available for this purpose, and the 3D structure of Aurora Kinase C was modeled and used to identify drugs that could interact with this target using docking-based virtual screening.
Journal ArticleDOI
AI Meets Exascale Computing: Advancing Cancer Research With Large-Scale High Performance Computing.
Tanmoy Bhattacharya,Thomas Brettin,James H. Doroshow,Yvonne A. Evrard,Emily J. Greenspan,Amy L. Gryshuk,Thuc T Hoang,Carolyn B Vea Lauzon,Dwight V. Nissley,Lynne Penberthy,Eric Stahlberg,Rick Stevens,Rick Stevens,Frederick H. Streitz,Georgia D. Tourassi,Fangfang Xia,George Zaki +16 more
TL;DR: The National Cancer Institute - Department of Energy collaboration, Joint Design of Advanced Computing Solutions for Cancer (JDACS4C), a multi-institution collaborative effort focused on advancing computing and data technologies to accelerate cancer research on three levels: molecular, cellular, and population is reviewed.
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.
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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.
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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.
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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.
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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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