There is considerable
activity and interest within the biological research community towards
using microarray analysis as a tool for the prediction of genetically
hetereogenious diseases: specifically for predicting prognosis and
treatment outcomes for such diseases as cancer (e.g. IOEC).
Statistical analysis and dendrograms have proven to deliver some degree
of success, but the utility of supervised machine learning algorithms
in general has not been explored very much.
This project will explore
application of the machine learning algorithms in WEKA to microarray data.
Specifically, we will start with the well-documented problem of
predicting a prognosis of ovarian cancer from the MC160 data (MC160)
This project is appropriate for students
with an interest in the application of machine learning to biological
problems. Good JAVA programming skills are essential.
Department of Computer
of Waikato, Hamilton, New Zealand