Omics, Data Analysis and Integration


The research group Omics Data Analysis and Integration investigates molecular mechanisms of plant phenotype emergence by means of large-scale data integration on the genomic, transcriptomic, metabolomic, and phenomic levels. We apply machine learning and graph theory to big data to discover new gene functions, and interactions between genes, metabolites and phenotypes.

Research Topics

While the advanced analytical platforms, including NGS, environment sensors, high throughput imaging or mass spectrometry, enable unprecedented depth of characterization of every single plant, the question about a potential of a given genotype to exhibit specific phenotypic trait remains either unanswered or is purely empirical. This is directly related to a very limited information about molecular effects of genetic variation and its propagation through molecular networks.

Thus, our major research focus is development and training of interpretable deep learning models that link multiple aspects of DNA sequence information with their effects in the molecular and macro-scale. We are particularly interested in crops, for which we reconstruct quantitative links between genetic variation, gene regulation, metabolism and quality traits of crops, merging quantitative genetics approaches with machine learning.



Dr. Jędrzej Jakub Szymański


Building 14.6y / Room 4044

+49 2461/61-85852



No results found.

Last Modified: 03.05.2024