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

cleansing methods were used to generate the ProbeSet values. The same 3 classifications
algorithms were employed as described above.
Figure 3.4: Candidate lists- Stearman predict Bhattacharjee. The genes from the original authors’ papers
proposed significant ProbeSet lists, further limited by those that met the BaFL cleansing criteria but not the
Welch’s t-test significance criterion, with ProbeSet values provided by one of the three data cleansing
methods: these values were used as input to the three classifier models. Each graph shows the number of
the ProbeSet value-generation method on the y-axis (described below) and the cumulative AUC value on
the x-axis. Each row of graphs shows outcomes for a particular initial candidate ProbeSet list. Each column
of graphs shows the results for a particular classifier, indicated in the graph. The red line highlights the 0.9
cumulative AUC. In this case the node indicates average AUC and the bars show the standard deviation for
100 random sampling iterations, with replacement. The final column summarizes each cleansing model’s
list interpretation, across the 3 classification algorithms. The final row of graphs summarizes the individual
algorithms, per cleansing methodology, across all four 4 candidate lists and follows the same color scheme.
Figure 3.5 is the reverse experiment, where the gene list derived from the Bhattacharjee
experiment was used for training the classification model, which was then tested on the Stearman
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