Comprehensive assessment of DNA content feature using machine learning approach

Sina, Nazeri (2016) Comprehensive assessment of DNA content feature using machine learning approach. Masters thesis, UNIMAS.

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Transcription Factor Proteins-DNA interactions play the key role in gene regulation. Identification of the regulatory elements or motifs bound by transcription factor proteins is critical to understand the gene regulatory network, diseases, and for medical benefit. Computational motif analysis, specifically the distal regulatory elements –enhancers– is notoriously difficult. Firstly, there are limited choices of features associated with it for machine learning task. Secondly, the discriminative feature that describes enhancer regions are ill-understood and no prior knowledge can be used in the design of recognition system. Lastly, different development stages and different cell lines activate different subset of enhancers which complicate computational methods of making conclusive results on the discriminative feature set that is used to model the active enhancers. Epigenetic and chromatin landmarks have been employed with great success to infer locations of enhancer regions as their locations have high correlation with enhancer regions. K-mer feature representation is one prominent approach for DNA content representation.

Item Type: Thesis (Masters)
Additional Information: Thesis (MSc.) -- Universiti Malaysia Sarawak, 2016.
Uncontrolled Keywords: Motifs, Enhancers, Epigenetic Marks, K-mer, Classification, Blended Model, unimas, university, universiti, Borneo, Malaysia, Sarawak, Kuching, Samarahan, ipta, education, Postgraduate, research, Universiti Malaysia Sarawak.
Subjects: H Social Sciences > H Social Sciences (General)
Q Science > QH Natural history > QH426 Genetics
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 03:19
Last Modified: 11 Jun 2020 08:57

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