Sample A: Cover Page of Thesis, Project, or Dissertation Proposal

classification models coinciding with down selection, as demonstrated in the last column. This
phenomenon mirrors what has been observed generally with Microarray data, the poor prediction
performance of proposed gene lists given different data and classification approaches [37, 42, 43].
Gains are much more consistent, if not large, when the BaFL-cleansed t-test down-selected data
are used (the third row of graphs in Figures 3.2 and 3.3).
Additionally, of the three models employed, Random Forest consistently did a poor job for the
RMA and dCHIP ProbeSets which were generated as the DE ProbeSets for the training model;
however, this was not observed with BaFL-generated values. This suggests that during the
stochastic development of the decision trees the selection of important features is often specific to
the dataset and not the disease condition. When the ProbeSet response is variable, its importance
to different models can either diminish, weakening its role as a classifier, or the regulation pattern
can be inverted, generating conflicting classifications. The RMA and dCHIP interpretations of
the datasets present 1.8% and 3.6% (respectively) of the intersecting DE ProbeSets with
conflicting regulation patterns between the two datasets, as shown in red in Figure 3.6. Linear
discriminant analysis, which weights all the features and hence allows variable genes to be more
or less important, did not show the same sensitivity as Random Forest.
84