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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Posted ContentDOI
Unveiling the molecular basis of disease co-occurrence: towards personalized comorbidity profiles
Jon Sánchez-Valle,Héctor Tejero,José M. Fernández,David Juan,Salvador Capella,Fatima Al-Shahrour,Rafael Tabarés-Seisdedos,Vera Pancaldi,Alfonso Valencia +8 more
TL;DR: A disease interaction network inferred from similarities in patients’ molecular profiles is presented, which significantly recapitulates epidemiologically documented comorbidities, providing the basis for their interpretation at a molecular level.
Book ChapterDOI
Drug Repurposing From Transcriptome Data: Methods and Applications
Daniel Toro-Domínguez,Marta E. Alarcón-Riquelme,Marta E. Alarcón-Riquelme,Pedro Carmona-Sáez +3 more
TL;DR: This chapter will describe in detail the main methods, applications, and computational resources for drug repositioning from transcriptome data, the type of data that has made the most progress in the field.
Posted ContentDOI
Detecting fabrication in large-scale molecular omics data
Michael Bradshaw,Samuel H. Payne +1 more
TL;DR: Methods of fabrication detection in biomedical research are developed and it is shown that machine learning can be used to detect fraud in large-scale omic experiments.
Journal ArticleDOI
Dissection of multiple sclerosis genetics identifies B and CD4+ T cells as driver cell subsets
Qiqi Miao,Wenfan Yan +1 more
TL;DR: In this article , the authors used chromatin accessibility data across hematopoietic cells to identify cell type-specific enrichments of MS genetic signals and found that CD4 T and B cells are independently enriched for MS genetics and further refined the driver subsets to T h 17 and memory B cells, respectively.
Journal ArticleDOI
CCSynergy: an integrative deep-learning framework enabling context-aware prediction of anti-cancer drug synergy
TL;DR: In this article , a flexible, context aware and integrative deep-learning framework is proposed for predicting drug synergy in cancer cell lines, based on the Chemical Checker extended drug bioactivity profiles.
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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