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.