Two biostat seminars at the Section of Biostatistics

You are all invited to TWO exciting seminars at Biostats in the very near future. Both seminars are held at the Biostats library.

On Monday, March 24 @ 10:00:

Speaker: Morten Mørup, Danish Technical University

Title: Characterizing Neural Responses, Individual Variability, and the Functional and Structural Organization of Connectomes using Machine Learning

Abstract

This talk will describe our research efforts towards using machine learning to extract patterns and account for individual variability in functional neuroimaging data. A primer will be given to machine learning and the importance of model generalization as well as how (non-parametric) Bayesian inference and predictive assessments can be used to evidence plausible hypothesis of brain organization. The talk will further highlight efforts towards characterizing individual variability in neural responses by use of deep learning voice conversion technologies adapted to the functional neuroimaging domain. Finally it will be discussed how machine learning can potentially be used to reduce the number of trials necessary in functional neuroimaging research.

and the day after we have

On Tuesday, March 25 @ 10:00:

Speaker: Benoit Liquet-Weiland, Macquarie University, School of Mathematical and Physical Sciences, Australia

Title: Sparse Group Variable Selection for Pleiotropic Association in High-Dimensional Genomic Context

Abstract

Genome-wide association studies (GWAS) have identified genetic variants associated with multiple complex diseases. We can leverage this phenomenon, known as pleiotropy, to integrate multiple data sources in a joint analysis. Often, integrating additional information such as gene pathway knowledge can improve statistical efficiency and biological interpretation. In this talk, I will review several frequentist and Bayesian statistical methods we have developed, which incorporate both gene pathway and pleiotropy knowledge to increase statistical power and identify important risk variants affecting multiple traits. Our methods are applied to identify potential pleiotropy in an application considering the joint analysis of thyroid and breast cancers.

For more information go to https://biostatistics.dk/seminars/.