Heterogeneous data fusion for alzheimer's disease study

Jieping Ye, Kewei Chen, Teresa Wu, Jing Li, Zheng Zhao, Rinkal Patel, Min Bae, Ravi Janardan, Huan Liu, Gene Alexander, Eric Reiman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

84 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationKDD 2008 - Proceedings of the 14th ACMKDD International Conference on Knowledge Discovery and Data Mining
Pages1025-1033
Number of pages9
DOIs
StatePublished - Dec 1 2008
Event14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2008 - Las Vegas, NV, United States
Duration: Aug 24 2008Aug 27 2008

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Other

Other14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2008
CountryUnited States
CityLas Vegas, NV
Period8/24/088/27/08

Keywords

  • Biomarker detection
  • Heterogeneous data source fusion
  • Multiple kernel learning
  • Neuroimaging
  • Tensor factorization

ASJC Scopus subject areas

  • Software
  • Information Systems

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  • Cite this

    Ye, J., Chen, K., Wu, T., Li, J., Zhao, Z., Patel, R., Bae, M., Janardan, R., Liu, H., Alexander, G., & Reiman, E. (2008). Heterogeneous data fusion for alzheimer's disease study. In KDD 2008 - Proceedings of the 14th ACMKDD International Conference on Knowledge Discovery and Data Mining (pp. 1025-1033). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). https://doi.org/10.1145/1401890.1402012