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Effect of Feature Extraction and Feature Selection on Expression Data from Epithelial Ovarian Cancer
Authors: Y. Turkeli, A. Ercil, 0. U. Sezerman
Published in: Proceedings of IEEE Engineering In Medicine And Biology Society Conference - EMBS 2003, Cancun, Mexico
Publication year: 2003
Abstract: Classifying the gene expression levels of normal
and cancerous cells and identifying the genes most contributing
to this distinction propose an alternative means of diagnosis.
We have investigated the effect of feature extraction and
feature selection on clustering of the expression data on two
different data sets for ovarian cancer. One data set consisted of
2176 transcripts from 30 samples, nine from normal ovarian
epithelial cells and 21 from cancerous ones. The other data set
had 7129 transcripts coming from 27 tumor and four normal
ovarian tissues. Hierarchical clustering algorithms employing
complete-link, average-link and Ward's method were
implemented for comparative evaluation. Principal component
analysis was applied for feature extraction and resulted in
100% segregation. Feature selection was performed to identify
the most distinguishing genes using CART@ software. Selected
features were able to cluster the data with 100% success. The
results suggest that adoption of feature extraction and selection
enhances the quality of clustering of gene expression data for
ovarian cancer, Identification of distinguishing genes is a more
complex problem that requires incorporating pathway
knowledge with statistical and machine learning methods.
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