Prediction of Hierarchical Classification of Transposable Elements Using Machine Learning Techniques

• Main Author: Panta, Manisha
• Format: Others
• Published: ScholarWorks@UNO 2019
• Subjects: Transposable Elements; Hierarchical Classification; Supervised Learning; Machine Learning; Computer Sciences; Other Computer Sciences; Physical Sciences and Mathematics
• Online Access: https://scholarworks.uno.edu/td/2677; https://scholarworks.uno.edu/cgi/viewcontent.cgi?article=3836&context=td

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.