An association rule analysis framework for complex physiological and genetic data

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He, Jing, Zhang, Yanchun, Huang, Guangyan, Xin, Yefei, Liu, Xiaohui, Zhang, Hao Lan, Chiang, Stanley and Zhang, Hailun (2012) An association rule analysis framework for complex physiological and genetic data. In: Health Information Science: First International Conference, HIS 2012, Beijing, China, April 8-10, 2012. Proceedings. He, Jing, Liu, Xiaohui, Krupinski, Elizabeth and Guandong, Xu, eds. Lecture Notes in Computer Science , 7231 . Springer, Heidelberg, Germany, pp. 131-142.


Physiological 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). 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.

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Item type Book Section
DOI 10.1007/978-3-642-29361-0_17
Official URL
ISBN 9783642293603 (print) 9783642293610 (online)
Subjects Historical > FOR Classification > 0604 Genetics
Historical > FOR Classification > 0806 Information Systems
Current > Division/Research > College of Science and Engineering
Keywords ResPubID25936, type II diabetes, T2DM, clinical phenotype, genotype, DNA/RNA sequencing
Citations in Scopus 3 - View on Scopus
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