Tools and Models

Our Department Computational Biology & Chemistry provides tools for robust and reproducible data analysis, focusing on AI and data science software to predict chemical-effect associations and protein-protein interactions. Our programs identify enriched pathways in multi-omics-data and we offer methods for curating and retrieving suitable datasets for research.

DeepFPlearn
an AI tool that predicts associations between chemicals and gene targets

DRomics Shiny Application
on-line tool for dose-response (or concentration-response) characterization from omics data

FishInspector
allows the user-friendly and easy annotation of features in zebrafish embryo 2-dimensional images

Indicate
Desktop application for assessing stress effects on ecosystems, including bioindicator tools and models for environmental data analysis

INTOB
is a software for the collection and management of data and metadata from toxicological observations according to the FAIR principles

LSER database
to calculate partition coefficients of chemicals

MassBank
an open access database for mass spectrometry reference spectra of the environmental and metabolomics domains

MLinvitroTox (project ExpectMine)
prediction of likelihood of bioactivity of molecular processes from high-resolution (fragmentation) mass spectra (de novo generation of chemical structures, robust evaluation of cell-based bioassay data, quantification without access to standards)

Multiple Coupled Compartements
to calculate the concentration-time curve of a chemical in 4 coupled compartments which are connected by reversible processes like diffusion, transformation and partitioning

Toxicogenomic Fingerprint Browser
a R Shiny tool for exploration and visualization of time and concentration dependent toxicogenomic data obtained with the zebrafish model

toxprofileR
a R package to work with dose and time dependent toxicogenomic data based on self-organising maps