Research question:

[1] How may commonly known genomic data integration approaches help to link chemical exposure to transcriptomic data. Is it possible to assume a single exposure scenario to identify biological meaningful and reliable findings in investigations with environmental relevant mixture exposure.

[2] Generate new hypotheses of mode of actions or adverse outcome pathways (AOPs), which describe effect of chemicals in the environment. Is it possible to generate a unified knowledge representation of ecotoxicological investigations, which may be used to predict unknown informations to fill knowledge gaps in the AOP-wiki?

Methodology and Approaches:

Data integration of transcriptomic and chemical data sets from UFZ-intern projects and open access data bases

Classification and assessment of chemical exposures

Method integration and evaluation

1. selforganizing maps
2. Gene correlation networks (WGCNA)
3. Association rule learning
4. Boolean networks
5. Deep learning (neural networks)
6. Text mining
7. Predictive toxicology