DNA enhancer prediction using machine learning techniques with novel feature representation

Fong, Pui Kwan (2016) DNA enhancer prediction using machine learning techniques with novel feature representation. PhD thesis, UNIMAS.

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Abstract

Identification of regulatory elements particularly enhancer region plays an important role in comprehending the regulation of gene expression. Current computational enhancer prediction tools are centred at Support Vector Machine (SVM) utilizing sequence content feature—the k-mer. While content feature is shown to be promising, it suffers from several critical weaknesses such as: 1) features associated with enhancer regions are ill-defined and poorly understood. The content feature is unable to represent the complex properties of deoxyribonucleic acid (DNA) sequences; 2) the k-mer feature represents only the global property of DNA sequences but not the localized property; and 3) lack of feature extraction, generation and selection techniques in the algorithm design. This dissertation aims to develop novel feature representations of histone DNA sequences which are associated with enhancer locations. Technical contributions of this study are: 1) complex tree-feature modelling using genetic algorithm (CTreeGA): Automated feature generation framework to capture patterns of interactions among short DNA segments in histone sequences.

Item Type: Thesis (PhD)
Additional Information: Thesis (Ph.D.) -- Universiti Malaysia Sarawak, 2016.
Uncontrolled Keywords: Machine Learning, DNA Enhancer Prediction, DNA Feature Representation, unimas, university, universiti, Borneo, Malaysia, Sarawak, Kuching, Samarahan, ipta, education, Postgraduate, research, Universiti Malaysia Sarawak. Feature Extraction Algorithm
Subjects: Q Science > Q Science (General)
Q Science > QM Human anatomy
Divisions: Academic Faculties, Institutes and Centres > Faculty of Cognitive Sciences and Human Development
Depositing User: Gani
Date Deposited: 30 Jul 2018 03:26
Last Modified: 11 Jun 2020 08:58
URI: http://ir.unimas.my/id/eprint/20988

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