Current microarray technology is able take a single tissue sample to construct an Affymetrix oglionucleotide array containing (estimated) expression levels of thousands of different genes for that tissue. The objective is to develop a more systematic approach to cancer classification based on Affymetrix oglionucleotide microarrays. For this purpose, I studied published colon cancer microarray data. Colon cancer, with 655,000 deaths worldwide per year, has become the fourth most common form of cancer in the United States and the third leading cause of cancer - related death in the Western world. This research has been focuses in two areas: class discovery, which means using a variety of clustering algorithms to discover clusters among samples and genes; and class prediction that refers to the process of developing a multi-gene predictor of class label for a sample using its gene expression profile. The accuracy of a predictor is also assessed by using it to predict the class of already known samples.
Date of Degree
Master of Science (MS)
Mathematics and Statistics
Science, Engineering and Technology
Liu, B. (2011). Class Discovery and Prediction of Tumor with Microarray Data [Master’s thesis, Minnesota State University, Mankato]. Cornerstone: A Collection of Scholarly and Creative Works for Minnesota State University, Mankato. https://cornerstone.lib.mnsu.edu/etds/180/
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