CLASSIFYING PEROXIREDOXIN SUBGROUPS AND IDENTIFYING DISCRIMINATING MOTIFS VIA MACHINE LEARNING

Accurate and automated functional annotation is a pressing open problem, with functional characterizations lagging far behind the exponential growth in biological sequence databases. In this thesis, I present our recent development of machine learning methods for high-throughput, accurate, sequence-based functional annotation. Chapter 1 describes the biological and computational background of this study. Chapter 2defines the specific problem we try to solve. Chapter 3 demonstrates that our 3mer-SVM, that accurately classifies Peroxiredoxin subgroups, can provide meaningful additional insight into the functional conserved sites in Peroxiredoxin protein. Moreover, in Chapter 4, we propose a two-round learning algorithm that can capture gapped-kmer features in sequences and lead to more accurate classifications than the kmer-SVM approach. We illustrate this learning algorithm can be useful as a de novo motif finder for uncovering discriminating motifs among sequences associated with particular activities and functions. With a brief discussion on the advantage and limitations on our kmer-based sequence classification and \textit{de novo} motif identification, in Chapter 5, we propose several potential applications for future directions.

Grantor dc:publisher

Wake Forest University

Year dc:date.issued

2018

Author dc:creator

• Xiao, Jiajie

Language dc:language.iso

en

Handle dc:identifier.uri

http://hdl.handle.net/10339/90706

OAI identifier oai:identifier

oai:wakespace.lib.wfu.edu:10339/90706