Foto del docente

Giulia Babbi

Junior assistant professor (fixed-term)

Department of Pharmacy and Biotechnology

Academic discipline: BIO/10 Biochemistry

Research

Keywords: Proteomics Database development Protein annotation Gene-Disease relation Rare diseases Machine learning Genomics Systematic Biology

Implementation of computational infrastructures and development of bioinformatics tools for the management and analysis of biological Big data

The project is funded within the European project H2020-CIRCLES. The research topics include: 1) the development of computational methods, including on artificial intelligence and machine learning approaches, for the annotation of structural and functional characteristics of genes, proteins and their variants; 2) the development of computational models for the characterization of complex biological processes through the integration of heterogeneous data, including genomic, metagenomic and proteomic data; 3) the development and maintenance of computational infrastructures that host computational databases and pipelines. The tools will be applied to the analysis of metagenomics data.

Implementation and curation of databases and tools for bioinformatics

eDGAR (edgar.biocomp.unibo.it), a resource for collecting and organizing data on gene-disease associations, for investigating the relations among genes associated with polygenic or heterogeneous disease.

PhenPath (phenpath.biocomp.unibo.it), a resource for associating phenotypes to biological pathways, and for the comparison of the genes/diseases/biological processes associated with different phenotypes.

International challenges for the critical assessment of bioinformatics resources

Participating in CAGI (Critical Assessment of Genome Interpretation) and in CAFA (Critical Assessment of Functional Annotation), ranking her methods among the top-scoring.

Protein modelling for disease case study

Applying techniques for protein modelling in real case studies, investigating the structural/functional properties of a protein to endow it with new features; in particular, HTT for Huntington disease and MYO1F for thyroid cancer.