The Faculty of Informatics and the Center for Computational Medicine in Cardiology (CCMC) are pleased to announce a seminar given by Fulvia Ferrazzi
Dynamic Bayesian networks to discover novel regulatory mechanisms underlying heart development
Universitätsklinikum Erlangen, Germany
Wednesday, February 10, 2016
USI Lugano Campus, room A13, red building (Via G. Buffi 13)
Gene networks offer a flexible framework to represent and analyze interactions between genes. Moreover, they can support the identification of novel hypotheses on regulatory processes. On the basis of measured expression data, the so-called ‘reverse engineering’ methodologies aim at inferring the underlying gene regulatory network. It has been shown that the introduction of prior knowledge in network learning can improve the accuracy of the inferred models. In the talk I will present the development of a Bayesian reverse engineering methodology to integrate prior knowledge in the learning of gene networks from temporal expression data. It was validated on gold-standard networks and used to analyze a rat heart development dataset profiling gene expression from embryonic to postnatal state at high resolution.
Dr. Fulvia Ferrazzi holds a Master’s degree in Computer Science and Engineering and a PhD in Bioengineering and Bioinformatics, both from the University of Pavia, Italy. As winner of an award for PhD students promoted by the MIT/Italy Consortium, she performed part of her PhD work at the Harvard Medical School/Massachusetts Institute of Technology, USA. In 2007 she was granted a 3-year Investigator Fellowship from the Centre for Communication and Research, Collegio Ghislieri, Pavia, to continue her research work. In 2011 she moved to the Gene Center, LMU Munich, to work in the systems biology group within the laboratory of Prof. U. Gaul and since July 2013 she has been a Research Associate at the Institute of Human Genetics, FAU, Germany. Her research focus is the development and application of data mining and probabilistic methods for genomics data analysis.