Publication Date
2021
Document Type
Dissertation/Thesis
First Advisor
Ryu, Duchwan
Degree Name
M.S. (Master of Science)
Legacy Department
Department of Statistics and Actuarial Science
Abstract
Genes perform vital roles in living beings. By taking charges of protein synthesis, genes are able to take control of the expression of living traits. There are a lot of diseases associated closely to our genes. By analyzing genetic information, we are able to detect or classify gene based diseases. Among genetic disease information technologies, microarray can be one of the widely used ones. Usually, microarray data records thousands of gene expression features from a small number of samples including both normal and abnormal expressed tissues. It provides standardized comparison information between normal and diseased tissues, so as to provide reference in classifying new diagnosing samples. Neural networks as a type machine learning method has strong power in computation and feature learning, which makes it a popular method analyzing large datasets. Microarray datasets generally have very large scale of learnable features, therefore, neural networks can be a good potential to analyze this type of data. While comparing with the large feature scales, the sample sizes turn out to be too small. Such type of high dimensional small sample sized data itself can be challengeable to deal with, not to mention about the overfitting problems.
In this thesis, a method of combining Bayesian statistics and self-organizing map with neural networks was proposed to analyze the high dimensional small sample sized microarray data in genetic disease classification. Where the self-organizing map would be used to reduce the dimensionality, while the Bayesian statistics would be applied to measure the parameter uncertainty from posterior distribution.
Recommended Citation
Zhou, Guangting, "Gene-Based Disease Classification Using Bayesian Self-Organizing Map Neural Networks" (2021). Graduate Research Theses & Dissertations. 7805.
https://huskiecommons.lib.niu.edu/allgraduate-thesesdissertations/7805
Extent
88 pages
Language
eng
Publisher
Northern Illinois University
Rights Statement
In Copyright
Rights Statement 2
NIU theses are protected by copyright. They may be viewed from Huskie Commons for any purpose, but reproduction or distribution in any format is prohibited without the written permission of the authors.
Media Type
Text
