Date of Award

Summer 8-5-2019

Degree Type

Thesis

Degree Name

M.S.

Degree Program

Computer Science

Department

Computer Science

Major Professor

Md Tamjidul Hoque

Second Advisor

Joel Atallah

Third Advisor

Christopher M. Summa

Fourth Advisor

Minhaz Zibran

Abstract

Transposable Elements (TEs) or jumping genes are the DNA sequences that have an intrinsic capability to move within a host genome from one genomic location to another. Studies show that the presence of a TE within or adjacent to a functional gene may alter its expression. TEs can also cause an increase in the rate of mutation and can even promote gross genetic arrangements. Thus, the proper classification of the identified jumping genes is important to understand their genetic and evolutionary effects. While computational methods have been developed that perform either binary classification or multi-label classification of TEs, few studies have focused on their hierarchical classification. The existing methods have limited accuracy in classifying TEs. In this study, we examine the performance of a variety of machine learning (ML) methods and propose a robust augmented Stacking-based ML method, ClassifyTE, for the hierarchical classification of TEs with high accuracy.

Rights

The University of New Orleans and its agents retain the non-exclusive license to archive and make accessible this dissertation or thesis in whole or in part in all forms of media, now or hereafter known. The author retains all other ownership rights to the copyright of the thesis or dissertation.

Available for download on Friday, August 05, 2022

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