Date of Submission

Summer 7-31-2018

Degree Type

Thesis

Degree Name

Master of Science in Computer Science (MSCS)

Department

Computer Science

Track

Others

Machine Learning

Faculty Advisor

Dr. Mingon Kang

Chair

Dr. Mingon Kang

Committee Member

Dr. Donghyun Kim

Committee Member

Dr. Chih-Cheng Hung

Abstract

Glioblastoma multiforme (GBM) is the most fatal malignant type of brain tumor with a very poor prognosis with a median survival of around one year. Numerous studies have reported tumor subtypes that consider different characteristics on individual patients, which may play important roles in determining the survival rates in GBM. In this study, we present a pathway-based clustering method using Restricted Boltzmann Machine (RBM), called R-PathCluster, for identifying unknown subtypes with pathway markers of gene expressions. In order to assess the performance of R-PathCluster, we conducted experiments with several clustering methods such as k-means, hierarchical clustering, and RBM models with different input data. R-PathCluster showed the best performance in clustering longterm and short-term survivals, although its clustering score was not the highest among them in experiments. R-PathCluster provides a solution to interpret the model in biological sense, since it takes pathway markers that represent biological process of pathways. We discussed that our findings from R-PathCluster are supported by many biological literatures. Keywords. Glioblastoma multiforme, tumor subtypes, clustering, Restricted Boltzmann Machine

Available for download on Thursday, July 30, 2020

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