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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Prediction of drug efficacy from transcriptional profiles with deep learning.
Jie Zhu,Jingxiang Wang,Xin Wang,Gao Mingjing,Bingbing Guo,Miaomiao Gao,Jiarui Liu,Yanqiu Yu,Liang Wang,Weikaixin Kong,Yongpan An,Zurui Liu,Xinpei Sun,Zhuo Huang,Hong Zhou,Ning Zhang,Ruimao Zheng,Zhengwei Xie +17 more
TL;DR: In this article, a deep learning-based efficacy prediction system (DLEPS) was proposed to identify drug candidates using a change in the gene expression profile in the diseased state as input.
Journal ArticleDOI
Polypharmacology or Promiscuity? Structural Interactions of Resveratrol With Its Bandwagon of Targets.
Uzma Saqib,Tanya T. Kelley,Siva K. Panguluri,Dongfang Liu,Rajkumar Savai,Mirza S. Baig,Stephan C. Schürer +6 more
TL;DR: It is suggested that resveratrol’s bioactivity is a result of scaffold promiscuity rather than polypharmacology, and the variety of binding modes across targets display little similarity in the pattern of target interaction.
Journal ArticleDOI
Integrative Pharmacogenomics Analysis of Patient-Derived Xenografts.
Arvind Singh Mer,Arvind Singh Mer,Wail Ba-alawi,Wail Ba-alawi,Petr Smirnov,Petr Smirnov,Yi Xiao Wang,Yi Xiao Wang,Ben Brew,Janosch Ortmann,Ming-Sound Tsao,Ming-Sound Tsao,David Cescon,Anna Goldenberg,Benjamin Haibe-Kains +14 more
TL;DR: This study demonstrates that Xeva provides a flexible platform for integrative analysis of preclinical in vivo pharmacogenomics data to identify biomarkers predictive of drug response, representing a major step forward in precision oncology.
Journal ArticleDOI
RAS-MAPK Reactivation Facilitates Acquired Resistance in FGFR1-Amplified Lung Cancer and Underlies a Rationale for Upfront FGFR-MEK Blockade.
Bruno Bockorny,Bruno Bockorny,Bruno Bockorny,Maria Rusan,Maria Rusan,Maria Rusan,Wankun Chen,Rachel G. Liao,Yvonne Y. Li,Yvonne Y. Li,Federica Piccioni,Jun Wang,Li Tan,Li Tan,Aaron R. Thorner,Tianxia Li,Yanxi Zhang,Changhong Miao,Therese Ovesen,Geoffrey I. Shapiro,David J. Kwiatkowski,Nathanael S. Gray,Matthew Meyerson,Matthew Meyerson,Peter S. Hammerman,Adam J. Bass +25 more
TL;DR: Dual blockade of FGFR and MEK proved to be a more powerful approach in preventing resistance across diverse FGFR dependencies and may represent a therapeutic opportunity to achieve durable responses to FGFR inhibition in FGFR-dependent cancers.
Journal ArticleDOI
A universal transcriptomic signature of age reveals the temporal scaling of Caenorhabditis elegans aging trajectories
Andrei E. Tarkhov,Ramani Alla,Srinivas Ayyadevara,Mikhail A. Pyatnitskiy,L. I. Menshikov,Robert J. Shmookler Reis,Peter Fedichev +6 more
TL;DR: The transcriptomic signature of age was used to identify possible life-extending drug compounds and successfully tested a handful of the top-ranking molecules in C. elegans survival assays and achieved up to a +30% extension of mean lifespan.
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.
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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.
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.
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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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