TUTORIAL
Gerald Schaefer (Loughborough University, UK)

Gerald Schaefer gained his BSc. in Computing from the University of Derby and his PhD in Computer Vision from the University of East Anglia. He worked at the Colour & Imaging Institute, University of Derby (1997-1999), in the School of Information Systems, University of East Anglia (2000-2001), in the School of Computing and Informatics at Nottingham Trent University (2001-2006), and in the School of Engineering and Applied Science at Aston University (2006-2009) before joining the Department of Computer Science at Loughborough University. His research interests are mainly in the areas of computational intelligence, medical imaging,colour image analysis, and the combination of these subject. He has published extensively in these areas with a total publication count exceeding 150. He is a member of the editorial board of 6 international journal, reviews for over 30 journals and serves on the programme committee of over 60 conferences. He is also the organiser of several international workshops and special sessions at conferences. His edited book on Computational Intelligence in Medical Imaging (CRC Press) will come out shortly, and several further edited books are due to be published in 2009 and 2010.

Tutorial Abstract:

Data Mining of Bioinformatics Data

Bioinformatics deals with biological data in order to increase our understanding of the underlying biological processes. However, extracting knowledge from bioinformatics data has proven to be challenging due to the vast amount of data to be analysed. One type of bioinformatics data is gene expression data which measure the levels of genes expressed in biological samples and it is this type of data that we will focus on in the tutorial. We will look at various data mining techniques to analyse gene expression data in an effective way. In particular, we will focus on two main tasks: gene expression clustering and classification and discuss various techniques that can be successfully employed for these. We will also look at some Matlab scripts that show simple applications of the techniques discussed to real gene expression data.

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