NDLI: Heterogeneous data fusion for alzheimer's disease study

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Heterogeneous data fusion for alzheimer's disease study

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Heterogeneous data fusion for alzheimer's disease study

Content Provider	ACM Digital Library
Author	Wu, Teresa Patel, Rinkal Bae, Min Alexander, Gene Zhao, Zheng Chen, Kewei Li, Jing Janardan, Ravi Liu, Huan Ye, Jieping Reiman, Eric
Abstract	Effective diagnosis of Alzheimer's disease (AD) is of primary importance in biomedical research. Recent studies have demonstrated that neuroimaging parameters are sensitive and consistent measures of AD. In addition, genetic and demographic information have also been successfully used for detecting the onset and progression of AD. The research so far has mainly focused on studying one type of data source only. It is expected that the integration of heterogeneous data (neuroimages, demographic, and genetic measures) will improve the prediction accuracy and enhance knowledge discovery from the data, such as the detection of biomarkers. In this paper, we propose to integrate heterogeneous data for AD prediction based on a kernel method. We further extend the kernel framework for selecting features (biomarkers) from heterogeneous data sources. The proposed method is applied to a collection of MRI data from 59 normal healthy controls and 59 AD patients. The MRI data are pre-processed using tensor factorization. In this study, we treat the complementary voxel-based data and region of interest (ROI) data from MRI as two data sources, and attempt to integrate the complementary information by the proposed method. Experimental results show that the integration of multiple data sources leads to a considerable improvement in the prediction accuracy. Results also show that the proposed algorithm identifies biomarkers that play more significant roles than others in AD diagnosis.
Starting Page	1025
Ending Page	1033
Page Count	9
File Format	PDF
ISBN	9781605581934
DOI	10.1145/1401890.1402012
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2008-08-24
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Neuroimaging Multiple kernel learning Biomarker detection Tensor factorization Heterogeneous data source fusion
Content Type	Text
Resource Type	Article

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