Invited Speakers

Invited Speaker

MAQC-II: microarrays in clinical practice. Conclusions and applications to breast cancer


August 27, 2009, 14:00-14:50
Vlad Popovici - Swiss Institute of Bioinformatics, Lausanne, CH
Location: Room 40, 28 Baritiu street, Faculty of Automation and Computer Science, Cluj-Napoca

Abstract:
MicroArray Quality Control (MAQC) project is an FDA-led effort whose goal is to assess the microarray technology with respect to the reproducibility and reliability of the generated gene signatures. While the first phase of the project focused on the technical aspects, demonstrating the high intra- and inter-platform reproducibility, the second phase (MAQC-II), which recently ended, investigated the development of multivariate gene expression-based prediction models. The main goal of the MAQC-II was to investigate the various sources of variability observed among models developed for the same pathology.

In the first part of the talk, the MAQC-II project will be presented and some general conclusions will be drawn. The second part will be dedicated to the presentation of an in-depth statistical analysis of one of the MAQC-II data sets - the breast cancer data set - and will allow a more detailed discussion of the model devlopment process.

Biography: Vlad Popovici

Vlad Popovici
Vlad Popovici is researcher with Swiss Institute of Bioinformatics (SIB), working on different theoretical and applied aspects of statistical pattern recognition. He has obtained his Engineering and M.Sc. degrees in computer science from Technical University of Cluj-Napoca, Romania, in 1998
and 1999, respectively, and his Ph.D. from Swiss Federal Institute of Technology (EPFL) in 2004.

Before joining SIB he has worked for Digital Media Institute, Tampere University of Technology as research scientist and for Signal Processing Institute at EPFL as assistant researcher and, later, as postdoctoral researcher. His current research focuses on various issues of machine learning like sparse classifiers, multiple classifier systems, error estimation in small sample size settings and their applications to life sciences.