Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/78113
Title: ECTracker-an efficient algorithm for haplotype analysis and classification
Authors: Lin, L. 
Wong, L. 
Leong, T.-Y. 
Lai, P. 
Keywords: classification
datamining
genetic variations
haplotypes
hemophilia A
Issue Date: 2007
Source: Lin, L.,Wong, L.,Leong, T.-Y.,Lai, P. (2007). ECTracker-an efficient algorithm for haplotype analysis and classification. Studies in Health Technology and Informatics 129 : 1270-1274. ScholarBank@NUS Repository.
Abstract: This work aims at discovering the genetic variations of hemophilia A patients through examining the combination of molecular haplotypes present in hemophilia A and normal local populations using data mining methods. Data mining methods that are capable of extracting understandable and expressive patterns and also capable of making predictions based on inferences made on the patterns were explored in this work. An algorithm known as ECTracker is proposed and its performance compared with some common data mining methods such as artificial neural network, support vector machine, naive Bayesian, and decision tree (C4.5). Experimental studies and analyses show that ECTracker has comparatively good predictive accuracies in classification when compared to methods that can only perform classification. At the same time, ECTracker is also capable of producing easily comprehensible and expressive patterns for analytical purposes by experts. © 2007 The authors. All rights reserved.
Source Title: Studies in Health Technology and Informatics
URI: http://scholarbank.nus.edu.sg/handle/10635/78113
ISBN: 9781586037741
ISSN: 09269630
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

35
checked on Feb 16, 2018

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.