Date of Award

Fall 12-13-2023

Degree Type


Degree Name

Master of Science (MS)


Computer Science

First Advisor

Dr. Murray Patterson

Second Advisor

Dr. Alex Zelikovsky

Third Advisor

Dr. Jonathan Shihao Ji


Cancer, a complex group of diseases characterized by abnormal cell growth, presents a significant global health challenge. Accurate classification of cancer types is vital for effective treatment and improved patient outcomes. This master’s thesis addresses the crucial issue associated with accurate cancer classification. It analyzes transcriptomic data of RNA sequencing, from six cancer subtypes (breast, colorectal, glioblastoma, hepatobiliary, lung, pancreatic) and a healthy control group. This research utilizes several machine learning algorithms to construct accurate cancer classification models using gene expression profiles and gene count data. The study incorporates advanced techniques such as feature selection, data preprocessing, and model optimization. The primary objective is to enhance our understanding of transcriptomic signatures distinguishing one cancer type from another, with potential applications in early diagnosis, treatment selection, and biomarker discovery. Through the power of machine learning, this research contributes to advancing effective cancer classification and management strategies in this ongoing battle.


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