The Connectivity Map: a new tool for biomedical research

By J. Lamb. Nature Reviews. Cancer, 2007

893 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/d1610ee5045d33be16a9a72a514fea537ba46306

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Citations in the corpus

Drug repositioning for non-small cell lung cancer by using machine learning algorithms and topological graph theory (2016, methodology): “The FC value of any gene expression level with a fold change value of less than 5.64 was set to 5.64 to facilitate the cMap [29] search.”
Human Disease-Drug Network Based on Genomic Expression Profiles (2009, background): “This enables an automated, somewhat comprehensive monitoring of the changes of various molecular components associated with different disease states and drug treatments, therefore enables the characterization of disease and drug effects, and an elucidation of their relationships at a molecular syste”
Computational approaches in target identification and drug discovery (2016, background): “…expression data from cultured human cells treated with bioactive small molecules and simple pattern-matching algorithms that together enable the discovery of functional connections between drugs, genes and diseases through the transitory feature of common gene-expression changes [26].”
Structure-based Systems Biology for Analyzing Off-target Binding (2011, background): “A number of computational methodologies have been developed where relate putative protein targets through their ligand chemistry [15,16] or biological profiles (e.g., binding activities [17], expression profiles [18,19], side-effects [20]).”
Discovery of drug mode of action and drug repositioning from transcriptional responses (2010, methodology): “Among the most promising approaches are the ones based on “ gene signatures ” (11, 12), i.e., subset of genes whose differential expression can be used as a marker of the activity of a given pathway, disease or compound.”
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): “In particular in the case of transcriptional pro-ﬁles, which have been related to disease phenotypes and models thereof (Lamb, 2007), these ﬁndings appear to support the feasibility of phenotypic lead optimization and utility of phenotypic structure-activity-relationships for drug development.”
Systematic integration of biomedical knowledge prioritizes drugs for repurposing (2017, background): “…et al., 2011 ; Li and Lu, 2012 ), disease-disease similarity ( Gottlieb et al., 2011 ; Chiang and Butte, 2009 ), transcriptional signatures ( Lamb, 2007 ; Qu and Rajpal, 2012 ; Cheng et al., 2014 ; Lamb et al., 2006 ; Iorio et al., 2013 ), protein interactions ( Guney et al., 2016 ),…”
Computational solutions for omics data (2013, background): “Large-scale gene expression investigations have had preliminary success at elucidating phenotypic gene expression signals 102–104 and applying those signals to downstream analyses, such as drug repurposing 104,105 .”