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Title page for ETD etd-11132017-115307


Type of Document Master's Thesis
Author Greer, Michael J.
URN etd-11132017-115307
Title Exploring the Utility of Ratio-based Co-expression Networks using a GPU Implementation of Semantic Similarity
Degree Master of Science
Department Biomedical Informatics
Advisory Committee
Advisor Name Title
Bing Zhang Committee Chair
Alissa Weaver Committee Member
Qi Liu Committee Member
Keywords
  • ratio
  • co-expression networks
  • semantic similarity
  • bioinformatics
  • gpu
Date of Defense 2017-11-01
Availability restricted
Abstract
The reduced cost of sequencing has made it feasible to acquire multi-tissue site expression data from the same patient. In the field of cancer research, this has caused an accumulation of cancer type specific tumor with matched adjacent normal expression data sets. Co-expression network analysis is a common technique used to analyze expression data; however, it is unknown whether integrating multi-tissue site data into network construction or constructing pan-cancer networks will improve gene function prediction. One method of evaluating network performance relies on semantic similarity scores; however, computing these scores is computationally intensive. Here, I develop a GPU implementation of a commonly used semantic similarity measure and evaluate its performance compared to CPU-based approaches. Next, I explore whether constructing co-expression networks using the ratio of tumor to match adjacent normal mRNA or a pan-cancer consensus network produces superior performance compared to networks constructed with tumor expression alone. The findings presented here indicate that the GPU-based approach offers significant performance improvement over CPU-based approaches. However, the ratio- and pan-cancer networks produce only a modest improvement over tumor-based networks.
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