For determination of the relationships among significant gene markers, statistical analysis
and association rule mining are considered as very useful protocols. The first protocol
identifies the significant differentially expressed/methylated gene markers, whereas the
second one produces the interesting relationships among them across different types of
samples or conditions. In this article, statistical tests and association rule mining based
approaches have been used on gene expression and DNA methylation datasets for the
prediction of different classes of samples (viz., Uterine Leiomyoma/class-formersmoker
and uterine myometrium/class-neversmoker). A novel rule-based classifier is proposed
for this purpose. Depending on sixteen different rule-interestingness measures, we have
utilized a Genetic Algorithm based rank aggregation technique on the association rules
which are generated from the training set of data by Apriori association rule mining algorithm.
After determining the ranks of the rules, we have conducted a majority voting
technique on each test point to estimate its class-label through weighted-sum method.
We have run this classifier on the combined dataset using 4-fold cross-validations, and
thereafter a comparative performance analysis has been made with other popular rulebased
classifiers. Finally, the status of some important gene markers has been identified
through the frequency analysis in the evolved rules for the two class-labels individually
to formulate the interesting associations among them.
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