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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Metabolomics-Driven Exploration of the Chemical Drug Space to Predict Combination Antimicrobial Therapies.
Adrian I. Campos,Mattia Zampieri +1 more
TL;DR: A combined experimental-computational approach to predict drug-drug interactions using high-throughput metabolomics revealed an unexpectedly large space of inhibited gene functions and enabled rational design of drug combinations.
Journal ArticleDOI
An integrative drug repositioning framework discovered a potential therapeutic agent targeting COVID-19.
Yiyue Ge,Tingzhong Tian,Huang S,Fangping Wan,Jingxin Li,Shuya Li,Wang X,Hui Yang,Lixiang Hong,Nian Wu,Enming Yuan,Yunan Luo,Lili Cheng,Chengliang Hu,Yipin Lei,Hantao Shu,Xiaolong Feng,Ziyuan Jiang,Yunfu Wu,Ying Chi,Xiling Guo,Lunbiao Cui,Liang Xiao,Zeng Li,Chunhao Yang,Zehong Miao,Ligong Chen,Ligong Chen,Haitao Li,Hainian Zeng,Dan Zhao,Fengcai Zhu,Fengcai Zhu,Xiaokun Shen,Jianyang Zeng +34 more
TL;DR: Wang et al. as discussed by the authors developed an integrative drug repositioning framework, which fully takes advantage of machine learning and statistical analysis approaches to systematically integrate and mine large-scale knowledge graph, literature and transcriptome data to discover the potential drug candidates against SARS-CoV-2.
Posted Content
Synergistic Drug Combination Prediction by Integrating Multi-omics Data in Deep Learning Models
TL;DR: A novel deep learning model is proposed, AuDNNsynergy, to predict the synergy of pairwise drug combinations by integrating multiomics data and outperforms four state-of-the-art approaches, namely, DeepSynergy, Gradient Boosting Machines, Random Forests, and Elastic Nets.
Journal ArticleDOI
Emerging Approaches for the Identification of Protein Targets of Small Molecules - A Practitioners' Perspective.
Kenneth M. Comess,Shaun M. McLoughlin,Jon A. Oyer,Paul L. Richardson,Henning Stöckmann,Anil Vasudevan,Scott E. Warder +6 more
TL;DR: This Perspective argues that data complexity can translate into meaningful decision-making and highlights a cohesive process that supports SM hit prosecution, providing a data-driven rationale and a suite of methods for direct identification of SM targets driving relevant biological end points.
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Insights into Computational Drug Repurposing for Neurodegenerative Disease.
TL;DR: This work examines existing approaches to computational drug repurposing, including molecular, clinical, and biophysical methods, and proposes data sources and methods to advance computational drugRepurposing in neurodegenerative disease using Alzheimer's disease as an example.
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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