Access Type
Open Access Thesis
Date of Award
January 2015
Degree Type
Thesis
Degree Name
M.S.
Department
Computer Science
First Advisor
Sorin Draghici
Abstract
It is accepted that many complex diseases, like cancer, consist in collections of distinct genetic diseases. Clinical advances in treatments are attributed to molecular treatments aimed at specific genes resulting in greater ecacy and fewer debilitating side effects. This proves that it is important to identify and appropriately treat each individual disease subtype. Our current understanding of subtypes is limited: despite targeted treatment advances, targeted therapies often fail for some patients. The main limitation of current methods for subtype identification is that they focus on gene expression, and they are subject to its intrinsic noise. Signaling pathways describe biological processes that are carried out by networks of genes interacting with each other. We developed PLSI, a software that allows to identify the specific pathways impacted in individual patients, subgroups of patients, or a given subtype of disease. The expected impact includes a better understanding of disease and resistance to treatment.
Recommended Citation
Donato, Michele, "Plsi: A Computational Software Pipeline For Pathway Level Disease Subtype Identification" (2015). Wayne State University Theses. 489.
https://digitalcommons.wayne.edu/oa_theses/489