Bioinformatics and Computational Biology; Gene regulatory networks; microRNAs; Non-coding DNA; Cancer and Plant genomics; Metagenomics; Big Data Analysis, Artificial Intelligence.

The work is based on an interdisciplinary approach, aiming to integrate analytical methods from mathematics, physics, and computer science to study biological systems, with a particular focus on genomic data.

Primary research interests lie in the characterization and evolution of regulatory networks, cancer and plant genomics, non-coding DNA, and metagenomics. In addition, the group supports research by designing, conducting, and interpreting high-throughput experiments across various life science domains. It develops and apply a wide range of big data analysis pipelines.

Recent efforts have focused on the development of machine learning and deep learning approaches, which are intended to be applied to the analysis of regulatory non-coding regions of genomes and to the interpretation of shotgun metagenomic data.