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Classifying Peroxiredoxin Subgroups and Identifying Discriminating Motifs via Machine Learning
Başlık:
Classifying Peroxiredoxin Subgroups and Identifying Discriminating Motifs via Machine Learning
Yazar:
Xiao, Jiajie, author.
ISBN:
9780355986945
Yazar Ek Girişi:
Fiziksel Tanımlama:
1 electronic resource (82 pages)
Genel Not:
Source: Masters Abstracts International, Volume: 57-06M(E).
Advisors: William H. Turkett Committee members: Grey Ballard; David John; James Pease.
Özet:
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 2 defines 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 de novo motif identification, in Chapter 5, we propose several potential applications for future directions.
Notlar:
School code: 0248
Tüzel Kişi Ek Girişi:
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Yer Numarası | Demirbaş Numarası | Shelf Location | Lokasyon / Statüsü / İade Tarihi |
---|---|---|---|
XX(692120.1) | 692120-1001 | Proquest E-Tez Koleksiyonu | Arıyor... |
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