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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Deep neural network prediction of genome-wide transcriptome signatures – beyond the Black-box
TL;DR: In this paper , the authors used a light-up technique to inspect the trained NN and found an over-representation of known TF-gene regulations, and the learned prediction network has a hierarchical organization.
Posted ContentDOI
Selective vulnerability of aneuploid human cancer cells to inhibition of the spindle assembly checkpoint
Yael Cohen-Sharir,James M. McFarland,Mai Abdusamad,Carolyn Marquis,Helen Tang,Marica Rosaria Ippolito,Sara Vanessa Bernhard,Kathrin Laue,Heidi L.H. Malaby,Andrew Jones,Mariya Kazachkova,Nicholas J. Lyons,Ankur K. Nagaraja,Ankur K. Nagaraja,Adam J. Bass,Adam J. Bass,Rameen Beroukhim,Rameen Beroukhim,Stefano Santaguida,Stefano Santaguida,Jason Stumpff,Todd R. Golub,Todd R. Golub,Zuzana Storchova,Uri Ben-David +24 more
TL;DR: A novel synthetic lethal interaction between aneuploidy and the SAC, which may have direct therapeutic relevance for the clinical application of SAC inhibitors is revealed and its cellular and molecular underpinnings are explored.
Posted ContentDOI
A Deep Learning Proteomic Scale Approach for Drug Design
TL;DR: In this article, a deep learning based autoencoder was used to first reduce the dimensionality of CANDO computed drug-proteome interaction signatures, and then employed a reduced conditional variational auto-encoder to generate novel drug-like compounds when given a target encoded "objective" signature.
Journal ArticleDOI
GraphDTI: A robust deep learning predictor of drug-target interactions from multiple heterogeneous data.
Guannan Liu,Manali Singha,Limeng Pu,Prasanga Neupane,Joseph Feinstein,Hsiao-Chun Wu,J. Ramanujam,Michal Brylinski +7 more
TL;DR: GraphDTI as discussed by the authors is a robust machine learning framework integrating the molecular-level information on drugs, proteins, and binding sites with the system level information on gene expression and protein-protein interactions.
Journal ArticleDOI
Systems Approach to Pathogenic Mechanism of Type 2 Diabetes and Drug Discovery Design Based on Deep Learning and Drug Design Specifications.
TL;DR: In this paper, a system biology approach was proposed to investigate the pathogenic mechanism for identifying significant biomarkers as drug targets and a systematic drug discovery strategy to design a potential multiple-molecule targeting drug for type 2 diabetes (T2D) treatment.
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.
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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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