DocumentCode :
3071812
Title :
Epistasy Search in Population-Based Gene Mapping Using Mutual Information
Author :
Saraee, Mohammad ; Nikoofar, Hamidreza ; Manzour, Amir
Author_Institution :
Univ. of Salford, Manchester
fYear :
2007
fDate :
15-18 Dec. 2007
Firstpage :
621
Lastpage :
625
Abstract :
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.
Keywords :
data mining; diseases; genetics; information theory; medical computing; AMD disease; Shannon mutual information; causal genetic region identification; epistasy search; pattern discovery; population-based gene mapping; relevance chains; Databases; Diseases; Entropy; Genetic communication; Information technology; Information theory; Mutual information; Signal mapping; Signal processing; Signal processing algorithms;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal Processing and Information Technology, 2007 IEEE International Symposium on
Conference_Location :
Giza
Print_ISBN :
978-1-4244-1835-0
Electronic_ISBN :
978-1-4244-1835-0
Type :
conf
DOI :
10.1109/ISSPIT.2007.4458203
Filename :
4458203
Link To Document :
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