Machine learning algorithms for making inferences on networks and answering questions in biology and medicine
28 ottobre 2019 alle 14.30
Sala del consiglio all'ottavo piano di via Celoria 18
Speaker: Prof. Alberto Paccanaro, del Department of Computer Science, Bioinformatics Centre for Systems and Synthetic Biology, Royal Holloway, University of London.
Responsabile scientifico: Giorgio Valentini
An important idea that has emerged recently is that a cell can be viewed as a set of complex networks of interacting bio-molecules and genetic disease is the result of abnormal interactions within these networks. In this talk, I will present novel machine learning algorithms for solving problems in systems biology and medicine that can be phrased in terms of inference in such large-scale networks.
I will begin by describing a method to accurately quantify a distance between disease modules on the human interactome that uses only disease phenotype information. I will then show how this measure can be exploited by a semi-supervised learning algorithm for inferring disease genes for heritable disease. Importantly, our approach allows the prediction of disease genes for diseases for which no disease gene is already known. Finally, I will present a method for the prediction of drug side effects. This algorithm, which is based on matrix factorization, is the first that can predict the frequency of drug side effects in the population.
Alberto Paccanaro is full Professor in Machine Learning and Computational Biology in the Department of Computer Science at Royal Holloway University of London where he is also Director of the Centre for Systems and Synthetic Biology. He completed his undergraduate studies in Computer Science at the University of Milan and received his PhD from the University of Toronto in 2002, specializing in machine learning under the supervision of Geoffrey Hinton. From 2002 to 2006, he was a postdoc in Mansoor Saqi’s lab at Queen Mary University of London and in Mark Gerstein’s lab at Yale University. His research interests are in applying and developing machine learning algorithms for solving problems in molecular biology and medicine.