C3BI Meeting – Seminar – Methodological – Modelling and visualization of high dimensional biomedical data

Finding biologically relevant structure and patterns in high dimensional multivariate data

Upcoming Events : Seminar – Methodological 06/18/2015 at 02:00 pm at Retrovirus room – LWOFF (22)

Date : 06/18/2015 at 02:00 pm Location : Retrovirus room – LWOFF (22) Speakers : Magnus Fontes, Head from International Group for Data Analysis at Institut Pasteur in Paris For any questions, suggestions (or to volunteer) for future talks or general feedback please contact us at bioinfo-hub@pasteur.fr

Modelling and visualization of high dimensional biomedical data

We will look at some different strategies for finding biologically relevant structure and patterns in high dimensional multivariate data. These strategies are all coupled to specific selections of underlying metrics and optimization of corresponding objective concentration functions. Please join us and share your experience, views and thoughts on the talk. We want this meeting to have an interactive and lively atmosphere with many debates.

C3BI

C3BI Meeting – Seminar – Methodological – Bayesian network approaches to modelling environmental and human health.

Upcoming Events : Seminar – Methodological 06/01/2015 at 02:00 pm at Jean-Paul Aubert room – FERNBACH (68)

Date : 06/01/2015 at 02:00 pm Location : Jean-Paul Aubert room – FERNBACH (68) Speakers : Mark Borsuk from Thayer School of Engineering and Institute for Quantitative Biomedical Sciences (iQBS), Dartmouth College, Hanover, NH, USA (https://engineering.dartmouth.edu/people/faculty/mark-borsuk) For any questions, suggestions (or to volunteer) for future talks or general feedback please contact us at bioinfo-hub@pasteur.fr

Bayesian network approaches to modelling environmental and human health.

Bioinformatics is concerned with the use of advanced computational methods to: (1) further our understanding of biological systems at all levels of detail and (2) support rational and evidence-based decision making concerning problem prevention, identification, and management. One integrative approach for pursuing these goals is Bayesian network (BN) modeling. By succinctly and effectively translating causal assertions into patterns of probabilistic dependence and vice versa, BNs facilitate logical and holistic reasoning under uncertainty in complex systems. Such structured probabilistic reasoning is necessary for accurate learning, analysis, synthesis, prediction, and decision making. I describe the BN approach, with reference to a number of examples from my own research in the areas of environmental and human health. Recent work in my group has focused on using BNs to reveal interactions between genetic and environmental risk factors in causing human disease. We have developed novel algorithms for learning the structure of a BN from a combination of prior knowledge and observational data. By capturing additive and non-additive interactions among multiple genetic and environmental risk factors, BN structures can improve understanding of biological processes as well as provide a tool for individualized risk assessment. Please join us and share your experience, views and thoughts on the talk. We want this meeting to have an interactive and lively atmosphere with many debates.

C3BI