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An association rule analysis framework for complex physiological and genetic data
conference contribution
posted on 2012-01-01, 00:00 authored by J He, Y Zhang, Guangyan HuangGuangyan Huang, Y Xin, X Liu, H L Zhang, S Chiang, H ZhangPhysiological and genetic information has been critical to the successful diagnosis and prognosis of complex diseases. In this paper, we introduce a support-confidence-correlation framework to accurately discover truly meaningful and interesting association rules between complex physiological and genetic data for disease factor analysis, such as type II diabetes (T2DM). We propose a novel Multivariate and Multidimensional Association Rule mining system based on Change Detection (MMARCD). Given a complex data set u i (e.g. u 1 numerical data streams, u 2 images, u 3 videos, u 4 DNA/RNA sequences) observed at each time tick t, MMARCD incrementally finds correlations and hidden variables that summarise the key relationships across the entire system. Based upon MMARCD, we are able to construct a correlation network for human diseases. © 2012 Springer-Verlag.
History
Event
International Conference on Health Information Science (1st : 2012 : Beijing, China)Volume
7231Series
Lecture Notes in Computer SciencePagination
131 - 142Publisher
SpringerLocation
Beijing, ChinaPlace of publication
Berlin, GermanyPublisher DOI
Start date
2012-04-08End date
2012-04-10ISSN
0302-9743eISSN
1611-3349ISBN-13
9783642293610Language
engPublication classification
E Conference publication; E1.1 Full written paper - refereedCopyright notice
2012, SpringerEditor/Contributor(s)
J He, X Liu, E Krupinski, G XuTitle of proceedings
First International Conference, HIS 2012, Beijing, China, April 8-10, 2012. ProceedingsUsage metrics
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