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