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

implemented in order to demonstrate that our method results in data that
behaves more consistently upon down selection. Area under the receiver
operating curve will be reported for three separate classification algorithms:
random forest (RF), k-nearest neighbors (kNN), and linear discriminate
analysis (LDA). For comparison, similar datasets have been subjected to the
commonly accepted RMA and dCHIP probe cleansing algorithms.
3) The ultimate goal of any Microarray experiment is to generate a subset of
genes that either gives insight into a biological mechanism important to the
sample state or that gives a high rate of success in predicting the state of a
sample. Here we have predicted a set of genes of interest for a two class
experiment, normal tissues versus adenocarcinoma lung cancer tissues. This
set of genes demonstrates impressive latent structure with and across datasets,
as well as supervised classification performance for random forests, kNN and
LDA. Comparisons of these genes have been made using the intensity values
by the commonly used methods RMA and dCHIP, in place of ours. Finally,
the relevance to the biological state of the sample of particular members of
the gene list was investigated by literature review
4) Given the world wide incidence rates of non small cell lung cancer and that
incidences of NSCLC are increasing in individuals who have never smoked
[90], a multiclass NSCLC dataset was constructed from the Bhattacharjee
data. This dataset included the adenocarcinoma, squamous cell carcinoma
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