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
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Positive correlation between transcriptomic stemness and PI3K/AKT/mTOR signaling scores in breast cancer, and a counterintuitive relationship with PIK3CA genotype
Ralitsa R. Madsen,Emily C. Erickson,Oscar M. Rueda,Xavier Robin,Carlos Caldas,Alex Toker,Robert K. Semple,Bart Vanhaesebroeck +7 more
TL;DR: In this paper, a positive linear association between transcriptionally-inferred PI3K/AKT/mTOR signaling scores and stemness scores was found in two independent human breast cancer cohorts.
Journal ArticleDOI
Analysis of defective pathways and drug repositioning in Multiple Sclerosis via machine learning approaches.
TL;DR: The defective pathways suggest viral or bacterial infections as plausible mechanisms involved in MS development and confirmed coincidences with Epstein-Barr virus, Influenza A, Toxoplasmosis, Tuberculosis and Staphylococcus Aureus infections.
Journal ArticleDOI
Deep learning in drug discovery: an integrative review and future challenges
Heba Askr,Enas Elgeldawi,Heba Aboul Ella,Yaseen A.M.M. Elshaier,Mamdouh M. Gomaa,Aboul Ella Hassanien +5 more
TL;DR: In this paper , the authors present a systematic literature review (SLR) that integrates the recent DL technologies and applications in drug discovery including, drug-target interactions (DTI), drug-drug similarity interactions (DDIs), drug sensitivity and responsiveness, and drug-side effect predictions.
Journal ArticleDOI
Advancing computational biology and bioinformatics research through open innovation competitions.
Andrea Blasco,Andrea Blasco,Matthias Endres,Rinat A. Sergeev,Anup Jonchhe,N. J. Maximilian Macaluso,Rajiv Narayan,Ted Natoli,Jin Hyun Paik,Bryan Briney,Chunlei Wu,Andrew I. Su,Aravind Subramanian,Karim R. Lakhani,Karim R. Lakhani +14 more
TL;DR: This work presents the decision process and competition design considerations that lead to successful outcomes in computational biology and bioinformatics research as a model for researchers who want to use competitions and non-domain crowds as collaborators to further their research.
Posted ContentDOI
High-Dimensional Gene Expression and Morphology Profiles of Cells across 28,000 Genetic and Chemical Perturbations
TL;DR: In this article, the authors provide a collection of four datasets with both gene expression and morphological profile data useful for developing and testing multi-modal methodologies, which can be used for more than a dozen applications in drug discovery and basic biology research.
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.
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.
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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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