Correlation-based cluster-space transform for major adverse cardiac event prediction

Xiao, YiSchool of Engineering and Information Technology, The University of New South Wales, Canberra, ACT, Australia (author)

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.

Pham, Tuan DSchool of Engineering and Information Technology, The University of New South Wales, Canberra, ACT, Australia(Swepub:liu)tuaph15 (author)
Jia, XiupingSchool of Engineering and Information Technology, The University of New South Wales, Canberra, ACT, Australia (author)
Zhou, XiaoboCentre for Biotechnology and Informatics, The Methodist Hospital Research Institute & Cornell University, Houston, TX, USA (author)
Yan, HongDepartment of Electronic Engineering, City University of Hong Kong, Hong Kong (author)
School of Engineering and Information Technology, The University of New South Wales, Canberra, ACT, AustraliaCentre for Biotechnology and Informatics, The Methodist Hospital Research Institute & Cornell University, Houston, TX, USA (creator_code:org_t)

In:IEEE International Conference on Systems Man and Cybernetics (SMC): Institute of Electrical and Electronics Engineers (IEEE), s. 2003-200797814244658809781424465866

By the author/editor: Xiao, Yi; Pham, Tuan D; Jia, Xiuping; Zhou, Xiaobo; Yan, Hong

About the subject

NATURAL SCIENCES: NATURAL SCIENCES; and Computer and Inf ...; and Computer Vision ...

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