By using high-throughput genetics we determined molecular mechanism of action of a neurotoxic phospholipase A2 in yeast S. cerevisiae. Based on the results and with the help of bioinformatics tools we generated hypotheses, and tested them. With this study we demonstrated that yeast is an extremely useful organism also for identification of the molecular targets of neurotoxins. This enables us to use the approach developed in the project not only for the identification of genotoxic, but also neurotoxic substances.
COBISS.SI-ID: 23541287
We developed a computational method which allows the researchers to generate an accurate mechanistic model of a studied perturbagen on the basis of chemogenomic and gene expression data. We implemented the method as a web-site, accessible at http://www.biolab.si/perturbagen/.
COBISS.SI-ID: 23789607
Biomedical experiments often include complex description of the experimental outcome. Mutant strains and cells exposed to various chemicals or range of conditions may be demonstrate a phenotype that is described with a range of descriptors. We have proposed and successfully applied the method of subgroup discovery that can well address such data.
COBISS.SI-ID: 7367764
We have developed a software architecture for the new generation of data mining platforms, which is particularly useful for applications in bioinformatics and systems biology. The paper that we are citing is describing the evolution of such systems and compares a number of similar existing open source projects of this kind.
COBISS.SI-ID: 6280532
We propose a new computational approach to subgroup discovery from that with potentially many outcome variables. The proposed approach was successfully applied in chemogenomics to relate description of chemical structured and corresponding yeast (whole-genome, mutant-based) phenotypes.
COBISS.SI-ID: 7256404