Integrative visual data mining of biomedical data: Investigating cases in Chronic Fatigue Syndrome and Acute Lymphoblastic Leukaemia

Kennedy, P; Simoff, SJ; Catchpoole, DR; Skillicorn, DB; Ubaudi, F; Al-Oqaily, A

Integrative visual data mining of biomedical data: Investigating cases in Chronic Fatigue Syndrome and Acute Lymphoblastic Leukaemia

Kennedy, P

Simoff, SJ Catchpoole, DR Skillicorn, DB Ubaudi, F Al-Oqaily, A

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Publication Type:: Chapter
Citation:: 2008, 4404 LNCS pp. 367 - 388
Issue Date:: 2008-08-29

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Field	Value	Language
dc.contributor.author	Kennedy, P https://orcid.org/0000-0001-7837-3171	en_US
dc.contributor.author	Simoff, SJ	en_US
dc.contributor.author	Catchpoole, DR	en_US
dc.contributor.author	Skillicorn, DB	en_US
dc.contributor.author	Ubaudi, F	en_US
dc.contributor.author	Al-Oqaily, A	en_US
dc.date.issued	2008-08-29	en_US
dc.identifier.citation	2008, 4404 LNCS pp. 367 - 388	en_US
dc.identifier.isbn	3540710795	en_US
dc.identifier.isbn	9783540710790	en_US
dc.identifier.uri	http://hdl.handle.net/10453/7852
dc.description.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. © 2008 Springer-Verlag Berlin Heidelberg.	en_US
dc.relation.isbasedon	10.1007/978-3-540-71080-6_21	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Integrative visual data mining of biomedical data: Investigating cases in Chronic Fatigue Syndrome and Acute Lymphoblastic Leukaemia	en_US
dc.type	Chapter
utslib.citation.volume	4404 LNCS	en_US
utslib.for	080301 Bioinformatics Software	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	4404 LNCS	en_US

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. © 2008 Springer-Verlag Berlin Heidelberg.

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http://hdl.handle.net/10453/7852