Access Type

Open Access Thesis

Date of Award

January 2017

Degree Type


Degree Name



Computer Science

First Advisor

Sorin Draghici


The identification of pathways that are involved in a particular phenotype helps us understand the underlying biological processes. Traditional pathway analysis techniques aim to infer the impact on individual pathways using only mRNA levels. However, recent studies showed that gene expression alone is unable to capture the whole picture of biological phenomena. At the same time, MicroRNAs (miRNAs) are newly discovered gene regulators that have shown to play an important role in diagnosis, and prognosis for different types of diseases. Current pathway analysis techniques do not take miRNAs into consideration. In this project, we investigate the effect of integrating miRNA and mRNA expression in pathway analysis.

In order to analyze biological pathways using miRNA expression data, we developed a novel method that augments KEGG pathways with microRNAs targeting genes. To validate our method, we analyzed nine GEO datasets. We also performed the analyses using just mRNA as well as using the integrative state-of-the-art method (microGraphite) to compare the results. In each case, we monitored the position of the pathway describing the given condition. We observed that our method outperforms the state-of-the-art approach.