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

for kNN were (k=3, l=2, with the Euclidean distance), and default settings in R were used for RF
and LDA [16]. We chose to use three different methods in order to explore whether the
classification performance was specific to the classification algorithm, and these three were
selected specifically because they are the most commonly cited in Microarray analysis papers and
because their performance requires minimal parameter tuning [6, 11, 12, 20, 21, 23, 27, 28, 34,
35]. Linear discriminant analysis attempts to find the linear combination of features which best
separates the data into their distinct classes, by weighting the features based upon their ability to
separate the classes [8, 9, 20]. Conversely, kNN and RF classify samples based upon the
characteristics of closely neighboring samples [6, 36]. The entire ensemble of features is utilized
for the kNN algorithm while RF stochastically builds forests of classification tress based upon the
strongest classifying features [10, 11]. After training with values from one experiment, the
models were used in tests against the other experiment and the performance was assessed: that is,
the Bhattacharjee gene lists were used for training and then the models were used to predict the
Stearman sample classes, and vice versa, for each of the types of gene lists described above [6,
36, 37]. This led to 9 comparisons in which the Bhattacharjee data were used as the training set
(RMA, dCHIP and BaFL cleansing post t-test, against 3 types of models) and 9 comparisons in
which the Stearman data were used as the training set.
The same classification algorithms were invoked for the comparison of the author’s lists to the
purely BaFL-derived list of 325 DE ProbeSets. This set of experiments is designed to be similar
to that of the validation of a final candidate list. Here, we compared the 325 BaFL intersecting
DE ProbeSets, to the BaFL-allowed ProbeSets in the author’s published lists. Validation of a
candidate list necessitates perturbing the designed models over iterative analysis to approach a
reliable performance metric [6, 36, 37]. Perturbation of our models was done through random
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