Abstract

Differential Ratio data mining was used to perform knowledge discovery within the 2-DE proteomics data, incorporating the spatial and temporal components. How does the work advance the state-of-the-art?: Development of data mining technique that performs automatic discovery of interesting trends within large spatio-temporal data incorporating both spatial and temporal elements, and nonspatial/temporal elements that describe the data. A measure is also introduced to evaluate and rank discoveries. Motivation (problems addressed): Analysis of 2-DE proteomics data is presently undertaken manually since current automated techniques are unable to identify interesting trends successfully, due to the inability to incorporate the spatial and temporal elements. An automated knowledge discovery technique could prove very useful within important areas of proteomics research and other spatio-temporal datasets.