Einladung zum Vortrag im Kolloquium Technische Kybernetik

Statistical clustering for the modeling of biological systems

Dr. Fabian Theis
Computational Modeling in Biology
Institute of Bioinformatics, GSF
Munich

    Zeit: Dienstag· 27. 11. 2007 · 16:00 Uhr
    Ort: IST-Seminarraum 3.241 · Pfaffenwaldring 9 · Campus Stuttgart-Vaihingen

Abstract

Systems biology seeks to integrate different levels of information to understand how biological systems function. It begins with the study of genes and proteins using high-throughput techniques such as microarray measurements or mass spectrometry data. Although the experimental methods for obtaining such recordings are advanced thus generating large and multivariate data sets, the underlying employed statistical tools have not reached this level of sophistication. In this talk, we propose to use higher-order statistics, sparse modeling and spatiotemporal clustering methods to extract additional information from these large data sets. Extended multi-dimensional inverse models are employed to detect latent variables within the observations, which may then be analyzed using graph-theoretic techniques. The resulting information-theoretic models and algorithms have applications in a wide field ranging from genomics to biomedical data analysis in general, telecommunications and financial markets, and their implications for genomics, proteomics and metabolomics are yet to be fully understood.

Biographical Information

Fabian J. Theis obtained MSc degrees in Mathematics and Physics at the University of Regensburg in 2000. He also received a PhD degree in Physics from the same university in 2002 and a PhD in Computer Science from the University of Granada in 2003. He worked as visiting researcher at the department of Architecture and Computer Technology (University of Granada, Spain), at the RIKEN Brain Science Institute (Wako, Japan), at FAMU-FSU (Florida State University, USA) and at TUAT's Laboratory for Signal and Image Processing (Tokyo, Japan), and headed the 'signal processing & information theory' group at the Institute of Biophysics (Regensburg, Germany). Recently, he started working as Bernstein fellow leading a junior research group at the Bernstein Center for Computational Neuroscience, located at the Max Planck Institute for Dynamics and Self-Organisation at Göttingen. His research interests include statistical signal processing, linear and nonlinear independent component analysis, overcomplete blind source separation and biomedical data analysis.