Please use this identifier to cite or link to this item:
|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||Citation:||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)|
Show full item record
Files in This Item:
|LL PhD Thesis.pdf||387.05 kB||Adobe PDF|
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.