SSP Group Meeting

11am, 2 May, 2006
Room 4.03, Appleton Tower
CISA, School of Informatics
University of Edinburgh

A Hybrid Knowledge Discovery Architecture for Proteomics Data

James Malone

Proteomics is a field dedicated to the analysis and identification of proteins within an organism.  Within proteomics, two-dimensional electrophoresis (2-DE) is currently unrivalled as a technique to separate and analyse proteins from tissue samples.  The analysis of post-experimental data produced from this technique has been identified as an important step within this overall process.  Some of the long term aims of this analysis are to identify targets for drug discovery and proteins associated with specific organism states, e.g. cancer. The large quantities of high-dimensional data produced from such experimentation requires expertise to analyse, which results in a processing bottleneck, limiting the potential of this approach. Furthermore, this data often features spatial and temporal elements which adds further complexity.  I present an intelligent hybrid architecture compromising of a neural network, a fuzzy inference system and differential ratio data mining, for knowledge discovery on this proteomic spatio-temporal data.  The architecture is able to automatically classify interesting proteins with a low number of false positives and false negatives whilst outperforming comparable techniques in terms of classification accuracy.