Integrative Visual Data Mining of Biomedical Data: Investigating Cases in Chronic Fatigue Syndrome and Acute Lymphoblastic Leukaemia

Authors:
Paul Kennedy;Simeon J. Simoff;Daniel R. Catchpoole;David B. Skillicorn;Franco Ubaudi;Ahmad Al-Oqaily
Affiliations:
Faculty of Information Technology, University of Technology, Sydney, Australia NSW 2007;School of Computing and Mathematics, University of Western Sydney, Australia NSW 2007 and Faculty of Information Technology, University of Technology, Sydney, Australia NSW 2007;Faculty of Information Technology, University of Technology, Sydney, Australia NSW 2007 and The Oncology Research Unit, The Children's Hospital at Westmead, Westmead, Australia NSW 2145;School of Computing, Queen's University, Kingston, Canada;Faculty of Information Technology, University of Technology, Sydney, Australia NSW 2007;Faculty of Information Technology, University of Technology, Sydney, Australia NSW 2007
Venue:
Visual Data Mining
Year:
2008

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Abstract

This chapter presents an integrative visual data mining approach towards biomedical data. This approach and supporting methodology are presented at a high level. They combine in a consistent manner a set of visualisation and data mining techniques that operate over an integrated data set of several diverse components, including medical (clinical) data, patient outcome and interview data, corresponding gene expression and SNP data, domain ontologies and health management data. The practical application of the methodology and the specific data mining techniques engaged are demonstrated on two case studies focused on the biological mechanisms of two different types of diseases: Chronic Fatigue Syndrome and Acute Lymphoblastic Leukaemia, respectively. The common between the cases is the structure of the data sets.