Highlights
Francesca Vitali: Data fusion strategies for precision medicine and drug repurposing
25 July 2019 - 25 July 2019
IDSIA Meeting Room, Galleria 1, @14h30
Data fusion strategies for precision medicine and drug repurposing. Over the last few years, biomedical research and clinical practice have experienced an
incredible growth in terms of both amount and heterogeneity of data being collected and leveraged for different types of analysis. This data explosion represents a great opportunity to increase our knowledge about many biological mechanisms as well as to improve medical processes (i.e., diagnosis, prognosis, therapy). However, not all big data are created equal. The downside of data heterogeneity is it complicates integration analysis. For example, clinical record data is highly heterogeneous, sparsely annotated, and contains several measurement types and unstructured text fields, comprised of ambiguous statements as well as varying levels of certainty, whereas genomic and imaging data are crisp and densely annotated data with a low cardinality of distinct variables. Integrating these data is particularly challenging when the molecular measurements are not conducted on individual subjects. In order to take full advantage of the wide spectrum of biomedical data available, advanced data integration tools need to be applied. In this context, I will present data fusion strategies for precision medicine and drug repositioning from my own research. These methods will include an approach for the prediction of potential multi-target drug repurposing strategies and its performances when applied to triple negative breast cancer. A second method that will be presented computes patient similarities by integrating patient-specific genomic data and public biomedical knowledge through a matrix tri-factorization approach. Finally, I will present a network-based approach integrating genomic and drug data with Gene Ontology-based information theoretic semantic similarities for the suggestion of new drug repurposing candidates. These examples show the potential of developing new research hypotheses and conducting predictive and data interpolation operations.

The speaker

After completing her Bachelor's Degree and Master's Degree in Biomedical Engineering at the University of Pavia, Dr. Vitali received her PhD in Bioinformatics and Biomedical Engineering from the University of Pavia, Italy. Before joining the Lussier Research Group, she was a postdoctoral researcher at the Bioinformatics, Mathematical Modelling and Synthetic Biology (BMS) Lab at the University of Pavia under the mentorship of prof. Riccardo Bellazzi. Her work mainly focused on developing computational methods to support drug repurposing and polypharmacology.

During her career, Dr. Vitali developed strong programming experience with different languages and solid knowledge of data mining, statistics, graph theory, machine learning, and data integration techniques. One of the key aspects in these methods is their flexibility to make them suitable for use in different contexts. Her research was conducted in an expert multi­disciplinary collaborative team, which included collaborations with international laboratories and pharmaceutical industries such as Sanofi and AstraZeneca

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