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An Association Rule Analysis Framework for Complex Physiological and Genetic Data

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Book cover Health Information Science (HIS 2012)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7231))

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Abstract

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). 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.

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References

  1. Botstein, D., Risch, N.: Discovering genotypes underlying human phenotypes: past successes for mendelian disease, future approaches for complex disease. Nature Genetics 33, 228–237 (2003), doi: 10.1038

    Article  Google Scholar 

  2. He, J., Zhang, Y., Huang, G.: Multivariate association mining for genetics and physiological data related with T2DM. Health Information Science and Systems (October 2011) (accepted)

    Google Scholar 

  3. Christensen, K., Murray, J.: What genome-wide association studies can do for medicine. N Engl. J. Med. 356(11), 1169–1171 (2007)

    Article  Google Scholar 

  4. Klein, R.: Complement factor H polymorphism in age-related macular degeneration. Science 308, 385–389 (2005), PMID, 15761122

    Article  Google Scholar 

  5. Johnson, A., O’Donnell, C.: An open access database of genome-wide association results. BMC Medical Genetics 10(6) (2009)

    Google Scholar 

  6. CGEMS Data Access, Cancer Genetic Markers of Susceptibility, National Cancer Institute, U.S.A., http://cgems.cancer.gov/

  7. National Human Genome Research Institute, National Institutes of Health, http://www.genome.gov/

  8. Diabetes Genetics Initiative, http://www.braod.mit.edu/diabetes

  9. Ku, C.: The pursuit of GWA studies: where are we now? Journal of Human Genetics 55(4), 195–206 (2010)

    Article  Google Scholar 

  10. Sladek, R., Rocheleau, G., Rung, J., et al.: A GWAS identifies novel risk loci for type 2 diabetes. Nature 445(7130), 881–885 (2007)

    Article  Google Scholar 

  11. Welcome Trust Case Control Consortium, Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447(7145), 661–678 (2007)

    Google Scholar 

  12. Kuok, C.M., Fu, A., Wong, M.H.: Shatin, Mining fuzzy association rules in databases. ACM SIGMOD 27(1) (1998)

    Google Scholar 

  13. Georgii, E., et al.: Analyzing microarray data using quantitative association rules. Bioinformatics 21(2), ii123–ii129

    Google Scholar 

  14. Agrawal, R., Srikant, R.: Fast algorithm for mining association rules in large databases. In: VLDB 1994, Santiago, Chile, pp. 487–499 (1994)

    Google Scholar 

  15. Pei, J., Han, J., Mao, R.: CLOSET: An efficient algorithm for mining frequent closed itemsets. In: Proc. 2000 ACM-SIGMOD Int. Workshop Data Mining and Knowledge Discovery (DMKD 2000), Dallas, TX, pp. 11–20 (May 2000)

    Google Scholar 

  16. Grahne, G., Zhu, J.: Efficiently using prefix-trees in mining frequent itemsets. In: Proc. ICDM 2003 Int. Workshop on Frequent Itemset Mining Implementations (FIMI 2003), Melbourne, FL (November 2003)

    Google Scholar 

  17. Zaki, M., Hsiao, C.: CHARM: An efficient algorithm for closed itemset mining. In: Proc. 2002 SIAM Int. Conf. Data Mining (SDM 2002), Arlington, VA, pp. 457–473 (April 2002)

    Google Scholar 

  18. Burdick, D., Calimlin, M., Gehrke, J.: MAFIA: A maximal frequent itemset algorithm for transactional databases. In: Proc. 2001 Int. Conf. Data Engineering (ICDE 2001), Heidelberg, Germany, pp. 443–452 (April 2001)

    Google Scholar 

  19. Ying, et al.: Predicting source code changes by mining revision history. IEEE Trans. Software Engineering 30, 574–586 (2004)

    Article  Google Scholar 

  20. Zimmermann, et al.: Mining version histories to guide software changes. IEEE Trans. Software Eng. 31(6), 429–445 (2005)

    Article  Google Scholar 

  21. Wu, et al.: Re-examination of interestingness measures in pattern mining: a unified framework. Data Min. Knowl. Discov. 21(3), 371–397 (2010)

    Article  MathSciNet  Google Scholar 

  22. Goh, K., Cusick, M., Valle, D., et al.: The human disease network. Proc. Natl. Acad. Sci., USA 104, 8685–8690 (2007)

    Article  Google Scholar 

  23. Jimenez-Sanchez, G., Childs, B., Valle, D.: Human Disease Genes. Nature 409, 853–855

    Google Scholar 

  24. Childs, B., Valle, D.: Genetics, biology and disease. Annu. Rev. Genomics Hum. Genet. (1), 1–19 (2000)

    Google Scholar 

  25. Qiao, Z., He, J., Zhang, Y.: Multiple Time Series Anomaly Detection Based on Compression and Correlation Analysis: Algorithm and Medical Surveillance Case Study. In: 13th Asia Pacific Web Conference, Kunming, China (April 2012) (under review)

    Google Scholar 

  26. He, J., et al.: Cluster Analysis and Optimization in Color-Based Clustering for Image Abstract. In: ICDM Workshops 2007, pp. 213–218 (2007)

    Google Scholar 

  27. Huang, G., Ding, Z., He, J.: Automatic Generation of Traditional Style Painting by Using Density-Based Color Clustering. In: ICDM Workshops 2007, pp. 41–44 (2007)

    Google Scholar 

  28. MicroArray Gene Expression Markup Language Links, http://www.mged.org/Workgroups/MAGE/MAGEdescription2.pdf

  29. Zhang, Y., Pang, C., He, J.: On multidimensional wavelet synopses for maximum error bounds. In: MCDM 2009, Chengdu, China (2009)

    Google Scholar 

  30. Huang, G., He, J., Ding, Z.: Wireless Video-Based Sensor Networks for Surveillance of Residential Districts. In: Zhang, Y., Yu, G., Bertino, E., Xu, G. (eds.) APWeb 2008. LNCS, vol. 4976, pp. 154–165. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  31. Huang, G., He, J., Ding, Z.: Inter-frame change directing online clustering of multiple moving objects for video-based sensor networks. In: Web Intelligence/IAT Workshops 2008, pp. 442–446 (2008)

    Google Scholar 

  32. Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 2nd edn. (2006)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Brunel University, UK

    Xiaohui Liu

  2. NIT, Zhejiang University, China

    Hao Lan Zhang

  3. TLC Medical PTY LTD, Australia

    Stanley Chiang

  4. Step High Technology Co Ltd, China

    Hailun Zhang

Authors
  1. Jing He
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  2. Yanchun Zhang
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  3. Guangyan Huang
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  4. Yefei Xin
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  5. Xiaohui Liu
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  6. Hao Lan Zhang
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  7. Stanley Chiang
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  8. Hailun Zhang
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Editor information

Editors and Affiliations

  1. Victoria University, Melbourne, Australia

    Jing He

  2. School of Information Systems, Computing and Mathematics, Centre for Intelligent Data Analysis, Brunel University, St. John’s 103, UB8 3PH, London, UK

    Xiaohui Liu

  3. Departments of Radiology and Psychology, The University of Arizona, 1609 N. Campbell Avenue, 85724, Tucson, AZ, USA

    Elizabeth A. Krupinski

  4. Faculty of Health, Engineering and Science, School of Engineering and Science, Centre for Applied Informatics, Victoria University, Ballarat Road, Footscray, 8001, Melbourne City, VIC, Australia

    Guandong Xu

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© 2012 Springer-Verlag Berlin Heidelberg

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He, J. et al. (2012). An Association Rule Analysis Framework for Complex Physiological and Genetic Data. In: He, J., Liu, X., Krupinski, E.A., Xu, G. (eds) Health Information Science. HIS 2012. Lecture Notes in Computer Science, vol 7231. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29361-0_17

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  • DOI: https://doi.org/10.1007/978-3-642-29361-0_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29360-3

  • Online ISBN: 978-3-642-29361-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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