Publications Repository - Gdańsk University of Technology

Bisphenols are important environmental pollutants that are extensively studied due to different detrimental effects, while the molecular mechanisms behind these effects are less well understood. Like other environmental pollutants, bisphenols are being tested in various experimental models, creating large expression datasets found in open access storage. The meta‐analysis of such datasets is, however, very complicated for various reasons. Here, we developed an integrating statistical and machine‐learning model approach for the meta‐analysis of bisphenol A (BPA) exposure datasets from different mouse tissues. We constructed three joint datasets following three different strategies for dataset integration: in particular, using all common genes from the datasets, uncorrelated, and not co‐expressed genes, respectively.

Authors

• Nina Lukashina,
• Michael Williams,
• Elena Kartysheva,
• Elizaveta Virko,
• dr hab. inż. Błażej Kudłak link open in new tab ,
• Robert Fredriksson,
• Ola Spjuth,
• Helgi B. Schiöth