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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A Deep Learning Model for Cell Growth Inhibition IC50 Prediction and Its Application for Gastric Cancer Patients
Minjae Joo,Aron Park,Kyungdoc Kim,Won-Joon Son,Hyo Sug Lee,GyuTae Lim,Jinhyuk Lee,Jinhyuk Lee,Dae Ho Lee,Jungseok An,Jung Ho Kim,TaeJin Ahn,Seungyoon Nam +12 more
TL;DR: A 1-dimensional convolution neural network model, DeepIC50, is constructed to predict three drug responsiveness classes, based on 27,756 features including mutation statuses and various drug molecular fingerprints, which could possibly accurately predict drug responsiveness, to new compounds, in diverse cancer cell lines, in the drug discovery process.
Journal ArticleDOI
Computational analysis to repurpose drugs for COVID-19 based on transcriptional response of host cells to SARS-CoV-2.
TL;DR: In this article, the authors developed a novel integrative analysis approach to identify the related molecular signaling pathways within host cells, and repurposed drugs as potentially effective treatments for COVID-19, based on the transcriptional response of host cells.
Journal ArticleDOI
A Proteotranscriptomic-Based Computational Drug-Repositioning Method for Alzheimer's Disease.
Soo Youn Lee,Min-Young Song,Dain Kim,Chaewon Park,Da Kyeong Park,Dong Geun Kim,Jong Shin Yoo,Young Hye Kim +7 more
TL;DR: A proteotranscriptomic-based computational drug repositioning method based on inverse associations between disease- and drug-induced gene and protein perturbation patterns, incorporating pharmacogenomic knowledge is developed, suggesting a shortcut to discover new efficacy of drugs for AD.
Journal ArticleDOI
Cancer Stem Cell (CSC) Inhibitors in Oncology-A Promise for a Better Therapeutic Outcome: State of the Art and Future Perspectives.
TL;DR: Clinical investigations with CSC marker follow-up as a measure of clinical efficacy are needed to turn the "hype" into the 'hope' these new-age oncology therapeutics have to offer.
Machine Learning and Network Medicine approaches for Drug Repositioning for COVID-19.
Suzana de Siqueira Santos,Mateo Torres,Diego Galeano,Diego Galeano,María del Mar Sánchez,Luca Cernuzzi,Alberto Paccanaro,Alberto Paccanaro +7 more
TL;DR: In this article, two machine learning approaches for drug repurposing are presented for SARS-CoV-2 infection/replication in COVID-19 patients, one based on matrix factorization and the other based on network medicine.
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.
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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