Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/28256
Title: Efficient mining of haplotype patterns for disease prediction
Authors: LIN LI
Keywords: linkage disequilibrium mapping, haplotypes, data mining, pattern extraction, classification, disease prediction
Issue Date: 6-Jun-2008
Source: LIN LI (2008-06-06). Efficient mining of haplotype patterns for disease prediction. ScholarBank@NUS Repository.
Abstract: This thesis examines some of the existing knowledge extraction techniques when applied to haplotypes for disease gene location inference, genetic variation analysis and carrier detection. The main difficulties in pattern extraction for such cases include rarity of the sample haplotypes of interest and noise in the data collected. Firstly, we proposed an efficient method for inferring disease gene locations (also known as linkage disequilibrium mapping). We compared our method with some leading methods; detailed experimental studies and analysis show that our approach is efficient while maintaining good predictive accuracies. Secondly, we extend our method to support descriptive analysis and classification of haplotype patterns. Widely used machine learning methods were evaluated for the purpose of both descriptive analysis and classification. Experimental studies show that our method is capable of extracting useful patterns to support genetic variation analysis and at the same time producing good predictive accuracies to facilitate carrier detection.
URI: http://scholarbank.nus.edu.sg/handle/10635/28256
Appears in Collections:Ph.D Theses (Open)

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