Machine Learning Coffee seminar "Variable Selection From Summary Statistics"

This week's speaker at our Machine Learning Coffee seminar will be

Matti Pirinen, Assistant Professor/Academy Research Fellow, Department of Mathematics and Statistics/Faculty of Medicine/FIMM, University of Helsinki

Variable Selection From Summary Statistics

Abstract:  With increasing capabilities to measure a massive number of variables, efficient variable selection methods are needed to improve our understanding of the underlying data generating processes. This is evident, for example, in human genomics, where genomic regions showing association to a disease may contain thousands of highly correlated variants, while we expect that only a small number of them are truly involved in the disease process. I outline recent ideas that have made variable selection practical in human genomics and demonstrate them through our experiences with the FINEMAP algorithm (Benner et al. 2016, Bioinformatics).

1. Compressing data to light-weight summaries to avoid logistics and privacy concerns related to complete data sharing and to minimize the computational overhead.
2. Efficient implementation of sparsity assumptions.
3. Efficient stochastic search algorithms.
4. Use of public reference databases to complement the available summary statistics

Machine Learning Coffee seminars are weekly seminars held jointly by the Aalto University and the University of Helsinki. The seminars aim to gather people from different fields of science with interest in machine learning. Seminars will be held on Mondays at 9 am at Aalto University and the University of Helsinki every other week. At Aalto University, talks will be held in Konemiehentie 2, seminar room T5 and at the University of Helsinki in Kumpula, seminar room D123, unless otherwise noted. Talks will begin at 9:15 am and porridge and coffee will be served from 9:00 am.

Welcome!