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Correlation-based cluster-space transform for major adverse cardiac event prediction
School of Engineering and Information Technology, The University of New South Wales, Canberra, ACT, Australia.
School of Engineering and Information Technology, The University of New South Wales, Canberra, ACT, Australia.ORCID iD: 0000-0002-4255-5130
School of Engineering and Information Technology, The University of New South Wales, Canberra, ACT, Australia.
Centre for Biotechnology and Informatics, The Methodist Hospital Research Institute & Cornell University, Houston, TX, USA.
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2010 (English)In: IEEE International Conference on Systems Man and Cybernetics (SMC), 2010, IEEE , 2010, 2003-2007 p.Conference paper (Refereed)Text
Abstract [en]

This paper investigates the affect of variation of patterns in protein profiles to the identification of disease-specific biomarkers. A correlation-based cluster-space transform is applied to mass spectral data for predicting major adverse cardiac events (MACE). Training and testing data are transformed into cluster spaces by correlation distance based clustering, respectively. Data in the testing cluster that falls into a pair of training clusters is classified by a supervised classifier. Experiment results have shown that proteomic spectra of MACE which vary with certain patterns could be separated by the correlation-based clustering. The cluster-space transform allows better classification accuracy than single-clustered class method for separating disease and healthy samples.

Place, publisher, year, edition, pages
IEEE , 2010. 2003-2007 p.
Keyword [en]
classification; clustering; major adverse cardiac events; mass spectrometry
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-125014DOI: 10.1109/ICSMC.2010.5641728OAI: oai:DiVA.org:liu-125014DiVA: diva2:902785
Conference
IEEE International Conference on Systems Man and Cybernetics (SMC), Istanbul, Turkey, 10-13 Oct. 2010
Available from: 2016-02-12 Created: 2016-02-12 Last updated: 2016-02-22Bibliographically approved

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Pham, Tuan D
Computer Vision and Robotics (Autonomous Systems)

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