The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease

By J. Lamb, E. Crawford, David Peck, Joshua W. Modell, Irene C. Blat, Matthew J Wrobel, Jim Lerner, Jean-Philippe Brunet, Aravind Subramanian, K. Ross and 10 more. 2006

4,911 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology

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Citations in the corpus

Computational approaches in target identification and drug discovery (2016, background): “Another useful approach helping to identify functional connections between diseases, genes and drugs is the Connectivity Map [25].”
Integrated deep learned transcriptomic and structure-based predictor of clinical trials outcomes (2016, background): “In the dataset, expression was profiled for a thousand “milestone” genes (Lamb et al. 2006).”
Large-scale integration of small molecule-induced genome-wide transcriptional responses, Kinome-wide binding affinities and cell-growth inhibition profiles reveal global trends characterizing systems-level drug action (2014, background): “Although there are studies linking drug pharmacology to transcriptional responses (Lamb et al., 2006), the connection to drug targets and the chemical structure of drugs is underexplored, partially because of a lack of large-scale proﬁling data.”
Two distinct interstitial macrophage populations coexist across tissues in specific subtissular niches (2019, methodology): “our lung IM scRNA-seq data to the bulk RNA-seq data using connectivity MAP (cMAP) (12), which generates scores (as scaled dimensionless quantities) indicative of the degree of “closeness” of one cell subset to a defined signature gene set.”
PREDICT: a method for inferring novel drug indications with application to personalized medicine (2011, neutral): “Table is supplemented as an Excel file.”
Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, background): “…2004). mRNA expression patterns were the first system-wide descriptors of drug effects enabling target clustering, target identification, and prediction of the mechanism of action of new compounds (Marton et al., 1998; Hughes et al., 2000; Lamb et al., 2006; Iorio et al., 2009; Chua & Roth, 2011).”
DrugComboRanker: drug combination discovery based on target network analysis (2014, methodology): “We have thus developed a novel tool, DrugComboRanker, to prioritize drug combinations and uncover the underlying mechanisms of action.”
Ontology-Based Meta-Analysis of Global Collections of High-Throughput Public Data (2010, background): “prints’’ and gene expression profiles of different cell lines treated with diverse compounds were explored through meta-analysis of data generated on a highly standardized, single microarray platform [11].”
Gene Expression Value Prediction Based on XGBoost Algorithm (2019, background): “Considering that gene expressions are usually highly correlated, researchers conducted an in-depth analysis of gene expression profiles and found that ~1,000 genes can capture about 80% of the entire gene expression profile (Lamb et al., 2006).”
Automatic construction of a large-scale and accurate drug-side-effect association knowledge base from biomedical literature (2014, background): “The majority of these approaches leverage on known drug properties such as chemical similarity [15], molecular activity similarity [20], molecular docking [17], and gene expression profile similarity [13, 24].”
A community effort to assess and improve drug sensitivity prediction algorithms (2014, methodology): “0 was used to express the methylation for each CpG locus as a value between 0 (completely unmethylated) and to 1 (completely methylated) (over 27,000 CpGs); (6) RPPA.”