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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Prediction of Drug Targets for Specific Diseases Leveraging Gene Perturbation Data: A Machine Learning Approach
TL;DR: In this article , a machine learning-based approach was proposed to predict drug targets for specific diseases using omics data, and leveraged expression profiles from gene perturbations. But, there are limited approaches for predicting drug targets.
Journal ArticleDOI
Assessing the effects of antipsychotic medications on schizophrenia functional analysis: a postmortem proteome study
Rawan Alnafisah,James K. Reigle,Mahmoud Ali Eladawi,Sinead M. O’Donovan,Adam J. Funk,J. Meller,Robert E. McCullumsmith,Rammohan Shukla +7 more
TL;DR: Compared SCZ-altered and APD-influenced signatures derived from the same cohort can provide better insight into SCZ pathophysiology, and a hyperdopaminergic cortex and drugs targeting the cognitiveSCZ-symptoms are identified and discussed.
Posted Content
Analysis of Gene Interaction Graphs as Prior Knowledge for Machine Learning Models
Paul Bertin,Mohammad Hashir,Martin Weiss,Vincent Frappier,Theodore J. Perkins,Geneviève Boucher,Joseph Paul Cohen +6 more
TL;DR: The authors' analysis with random graphs finds that dependencies can be captured almost as well at random which suggests that, in terms of gene expression levels, the relevant information about the state of the cell is spread across many genes.
Journal ArticleDOI
Bioinformatics roadmap for therapy selection in cancer genomics
María José Jiménez-Santos,Santiago García-Martín,Coral Fustero-Torre,Tomas di Domenico,Gonzalo Gómez-López,Fatima Al-Shahrour +5 more
TL;DR: This review describes the current approaches for therapy selection depending on which type of tumour heterogeneity is being targeted and the available next-generation sequencing data and discusses how these therapy selection workflows could be integrated into the clinical practice.
Posted ContentDOI
DREIMT: a drug repositioning database and prioritization tool for immunomodulation
Kevin Troulé,Hugo López-Fernández,Santiago García-Martín,Miguel Reboiro-Jato,Carlos Carretero-Puche,Jordi Martorell-Marugán,Guillermo Martín-Serrano,Pedro Carmona-Sáez,Daniel Gonzalez-Peña,Fatima Al-Shahrour,Gonzalo Gómez-López +10 more
TL;DR: DREIMT provides significant immunomodulatory drugs targeting up to 70 immune cells subtypes through a curated database that integrates 4,960 drug profiles and ~2,6K immune gene expression signatures.
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.
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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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