Epistasy search in population-based gene mapping using mutual information
Saraee, M, Nikoofar, H and Manzour, A 2007, Epistasy search in population-based gene mapping using mutual information , in: 2007 IEEE International Symposium on Signal Processing and Information Technology, 15-18 December 2007 , Cairo, EGYPT.
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Gene mapping intends to identify the causal genetic regions of a specific phenotype mostly a complex disease. These diseases are believed to have multiple contributing loci that are potentially unknown and often have subtle patterns making them hard to find. Shannon's mutual information figure is used as a criterion. Algorithms based on this criterion as presented and discussed. Furthermore, an algorithm is proposed to form relevance chains. The proposed algorithms are especially in favor of diseases having almost equally contributing regions known as being epistatic and is applied to both simulated and real data. AMD disease results are included. Some highly associated markers are found in AMD. C# source files for relevance-chains are freely available at https://www. sharemation. com/amanzour.
|Item Type:||Conference or Workshop Item (Paper)|
|Additional Information:||Print ISBN: 978-1-4244-1835-0|
|Themes:||Health and Wellbeing|
|Schools:||Colleges and Schools > College of Science & Technology > School of Computing, Science and Engineering > Data Mining and Pattern Recognition Research Centre|
|Journal or Publication Title:||Proceedings of 2007 IEEE International Symposium on Signal Processing and Information Technology,|
|Depositing User:||Dr Mo Saraee|
|Date Deposited:||26 Oct 2011 14:52|
|Last Modified:||20 Aug 2013 18:16|
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