» Articles » PMID: 19788908

Application of Connectivity Mapping in Predictive Toxicology Based on Gene-expression Similarity

Overview
Journal Toxicology
Publisher Elsevier
Specialty Toxicology
Date 2009 Oct 1
PMID 19788908
Citations 25
Authors
Affiliations
Soon will be listed here.
Abstract

Connectivity mapping is the process of establishing connections between different biological states using gene-expression profiles or signatures. There are a number of applications but in toxicology the most pertinent is for understanding mechanisms of toxicity. In its essence the process involves comparing a query gene signature generated as a result of exposure of a biological system to a chemical to those in a database that have been previously derived. In the ideal situation the query gene-expression signature is characteristic of the event and will be matched to similar events in the database. Key criteria are therefore the means of choosing the signature to be matched and the means by which the match is made. In this article we explore these concepts with examples applicable to toxicology.

Citing Articles

Repeat-dose toxicity prediction with Generalized Read-Across (GenRA) using targeted transcriptomic data: A proof-of-concept case study.

Tate T, Wambaugh J, Patlewicz G, Shah I Comput Toxicol. 2023; 19:1-12.

PMID: 37309449 PMC: 10259651. DOI: 10.1016/j.comtox.2021.100171.


Network medicine framework shows that proximity of polyphenol targets and disease proteins predicts therapeutic effects of polyphenols.

do Valle I, Roweth H, Malloy M, Moco S, Barron D, Battinelli E Nat Food. 2023; 2(3):143-155.

PMID: 37117448 DOI: 10.1038/s43016-021-00243-7.


Navigating Transcriptomic Connectivity Mapping Workflows to Link Chemicals with Bioactivities.

Shah I, Bundy J, Chambers B, Everett L, Haggard D, Harrill J Chem Res Toxicol. 2022; 35(11):1929-1949.

PMID: 36301716 PMC: 10483698. DOI: 10.1021/acs.chemrestox.2c00245.


Identification of drug combinations on the basis of machine learning to maximize anti-aging effects.

Kim S, Goughnour P, Lee E, Kim M, Chae H, Yun G PLoS One. 2021; 16(1):e0246106.

PMID: 33507975 PMC: 7843016. DOI: 10.1371/journal.pone.0246106.


Considerations for Strategic Use of High-Throughput Transcriptomics Chemical Screening Data in Regulatory Decisions.

Harrill J, Shah I, Setzer R, Haggard D, Auerbach S, Judson R Curr Opin Toxicol. 2019; 15:64-75.

PMID: 31501805 PMC: 6733036. DOI: 10.1016/j.cotox.2019.05.004.