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

@article{Lamb2006TheCM,
  title={The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease},
  author={Justin Lamb and Emily D. Crawford and David Peck and Joshua W. Modell and Irene C. Blat and Matthew J Wrobel and Jim Lerner and Jean-Philippe Brunet and Aravind Subramanian and Kenneth N. Ross and Michael Reich and Haley Hieronymus and Guo Wei and Scott A. Armstrong and Stephen J. Haggarty and Paul A. Clemons and Ru Wei and Steven A. Carr and Eric S. Lander and Todd R. Golub},
  journal={Science},
  year={2006},
  volume={313},
  pages={1929 - 1935}
}
To pursue a systematic approach to the discovery of functional connections among diseases, genetic perturbation, and drug action, we have created the first installment of a reference collection of gene-expression profiles from cultured human cells treated with bioactive small molecules, together with pattern-matching software to mine these data. [] Key Result These results indicate the feasibility of the approach and suggest the value of a large-scale community Connectivity Map project.

Revisiting Connectivity Map from a gene co-expression network analysis

TLDR
The analysis developed in the present study may provide a novel framework for drug repositioning or discovering MoAs, and an example of disease-based module projection to identify novel drugs was provided.

C2Maps: a network pharmacology database with comprehensive disease-gene-drug connectivity relationships

TLDR
The C2Maps platform is an online bioinformatics resource that provides biologists with directional relationships between drugs and genes/proteins in specific disease contexts based on network mining, literature mining, and drug effect annotating.

Connecting gene expression data from connectivity map and in silico target predictions for small molecule mechanism-of-action analysis.

TLDR
This paper spotlights the integration of gene expression data and target prediction scores, providing insight into mechanism of action (MoA) in compounds and shows one or more underlying MoA for compounds that were well-substantiated with literature.

Building Disease-Specific Drug-Protein Connectivity Maps from Molecular Interaction Networks and PubMed Abstracts

TLDR
A computational framework to build disease-specific drug-protein connectivity maps based on protein interaction networks and literature mining was developed, showing that this molecular connectivity map development approach outperformed both curated drug target databases and conventional information retrieval systems.

A simple and robust method for connecting small-molecule drugs using gene-expression signatures

TLDR
This work has built on the principles of the Connectivity Map to present a simpler and more robust method for the construction of reference gene-expression profiles and for the connection scoring scheme, which importantly allows the valuation of statistical significance of all the connections observed.

Genes2FANs: connecting genes through functional association networks

TLDR
The finding that disease genes in many cancers are mainly connected through PPIs whereas other complex diseases, such as autism and type-2 diabetes, are mostly connected through FANs without PPIs can guide better strategies for disease gene discovery.

Network-Based Prioritization of Disease Genes, Animal Models, and Drug Targets

TLDR
Three network-based computational approaches for human disease research and gaining insight into the mode of action of drugs are developed, one of which can detect critical genes and pathways targeted by a drug treatment from gene expression data even in the absence of large-scale expression differences.

The Use of Large-Scale Chemically-Induced Transcriptome Data Acquired from LINCS to Study Small Molecules.

TLDR
This chapter presents a method for pathway enrichment analyses of regulated genes to reveal biological pathways activated by compounds, and a method using the pre-knowledge on chemical-protein interactome for predicting potential target proteins, including primary targets and off-targets, with transcriptional similarity.

Comparing gene expression similarity metrics for connectivity map

  • Jie ChengLun Yang
  • Computer Science
    2013 IEEE International Conference on Bioinformatics and Biomedicine
  • 2013
TLDR
This work evaluates different gene expression profile similarity metrics by comparing their ability to predict a compound's chemical grouping using the Anatomical Therapeutic Chemical (ATC) drug classification system and shows that the simple eXtreme sum (XSum) and eXTreme cosine (XCos) measures perform significantly better than the standard Kolmogorov-Smirnov (KS) statistic.
...

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