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

fold decrease in the number of candidate genes is important for diagnostic applications [6, 41]. It
is also useful that there is no great dependence on a particular model for doing the classification.
Most notably, we see very good cross-experiment performance, even with quite limited candidate
gene lists, a rare achievement with Microarray data.
Author’s List (Validation)
Of the 72 AUC scores recorded for classification performance, the values generated by the BaFL
pipeline when used for candidate gene lists achieved 5 of 6 best overall scores (87-97%) over the
three classification algorithms, although not all of these are significant given the variance in the
results. The 6 th case, and the exception, was the kNN results when Bhattacharjee DE candidate
genes, using the RMA generated values, were used to predict Stearman data (93.88%). In
addition the BaFL-defined DE ProbeSets achieve the highest AUC for almost every individual
analysis of the BaFL intersecting DE and Stearman list, again the sole exception is the kNN
implementation of Bhattacharjee predicting Stearman. If you average the performance across
the lists per cleansing routine, values obtained using the BaFL method achieved the highest
performance 5 of 6 times (bottom row of Figures 3.4 and 3.5). Equal or improved performance
across experiments, with less variability and smaller candidate gene lists, and low sensitivity to
the model, are diagnostic goals that the BaFL method appears to be well positioned to achieve.
We note that a possible cause for the relatively poorer BaFL performance for Bhattacharjee
predict Stearman implementation of kNN, may be the result of random replicate removal out of
the small Stearman dataset, coupled with the absence of scaling across arrays in the BaFL
pipeline, as compared to RMA and dCHIP pipelines, which do incorporate scaling steps. We
support this statement by noting that the degradation of performance was not observed in the
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