2026-09-19T23:23:53Zhttps://wakespace.lib.wfu.edu/server/oai/request

oai:null:10339/907062026-09-02T15:20:03Zcom_10339_14934col_10339_38132

Xiao, Jiajie 2018-05-24T08:36:02Z 2019-05-23T08:30:11Z 2018 https://wakespace.lib.wfu.edu/handle/10339/90706 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. en Wake Forest University CLASSIFYING PEROXIREDOXIN SUBGROUPS AND IDENTIFYING DISCRIMINATING MOTIFS VIA MACHINE LEARNING Thesis Turkett, William H John, David Ballard, Grey Pease, James Computer Science 2019-05-23 open.access