ArticleFrontiers in genetics2018
Network and Pathway Analysis of Toxicogenomics Data.
Article in Frontiers in genetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 37 citations in OpenAlex.
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- The human hepatocyte TXG-MAPr: gene co-expression network modules to support mechanism-based risk assessment.Archives of toxicology · 2021Article
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- Network integration and modelling of dynamic drug responses at multi-omics levels.Communications biology · 2020Article
- NetCore: a network propagation approach using node coreness.Nucleic acids research · 2020Article
- Transcriptomics in Toxicogenomics, Part III: Data Modelling for Risk Assessment.Nanomaterials (Basel, Switzerland) · 2020Review
- Assessment of the Mode of Action Underlying the Effects of GenX in Mouse Liver and Implications for Assessing Human Health Risks.Toxicologic pathology · 2020Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Toxicogenomics is the study of the molecular effects of chemical, biological and physical agents in biological systems, with the aim of elucidating toxicological mechanisms, building predictive models and improving diagnostics. The vast majority of toxicogenomics data has been generated at the transcriptome level, including RNA-seq and microarrays, and large quantities of drug-treatment data have been made publicly available through databases and repositories. Besides the identification of differentially expressed genes (DEGs) from case-control studies or drug treatment time series studies, bioinformatics methods have emerged that infer gene expression data at the molecular network and pathway level in order to reveal mechanistic information. In this work we describe different resources and tools that have been developed by us and others that relate gene expression measurements with known pathway information such as over-representation and gene set enrichment analyses. Furthermore, we highlight approaches that integrate gene expression data with molecular interaction networks in order to derive network modules related to drug toxicity. We describe the two main parts of the approach, i.e., the construction of a suitable molecular interaction network as well as the conduction of network propagation of the experimental data through the interaction network. In all cases we apply methods and tools to publicly available rat
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