B0791
Title: An EM algorithm for penalized mixed-effects multitask learning: A general framework for regularizing MLM Models
Authors: Francesca Ieva - Politecnico di Milano (Italy)
Andrea Cappozzo - Politecnico di Milano (Italy) [presenting]
Giovanni Fiorito - Universita di Sassari (Italy)
Abstract: Linear mixed modeling is a well-established technique widely employed when observations possess a grouping structure. Nevertheless, this standard methodology is no longer applicable when the learning framework encompasses a multivariate response and high-dimensional predictors. To overcome these issues, a penalized estimation scheme based on an expectation-maximization (EM) algorithm is devised. Any penalty criteria for fixed-effects models can be conveniently incorporated into the fitting process. We employ the novel methodology for creating surrogate biomarkers of cardiovascular risk factors, such as lipids and blood pressure, from whole-genome DNA methylation data in a multi-center study. The described method performs better than state-of-the-art alternatives, both in terms of predictive power and bio-molecular interpretation of the results.
