Identifying Cancer Subtypes Using Unsupervised Deep Learning

<p>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</p>

Name thesis:degree_name

Master of Science in Computer Science (MSCS)

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Year dc:date.available

2018

Author dc:creator

• Mallavarapu, Tejaswini

Contributors dc:contributor

• Dr. Mingon Kang
• Dr. Donghyun Kim

• R-PathCluster
• SPACL
• Restricted Boltzmann Machine
• Deep Belief Network

Repository record dc:identifier

https://digitalcommons.kennesaw.edu/cs_etd/12

OAI identifier oai:identifier

oai:digitalcommons.kennesaw.edu:cs_etd-1014