Joint identification of imaging and proteomics biomarkers of alzheimer's disease using network-guided sparse learning

Jingwen Yan, Heng Huang, Sungeun Kim, Jason Moore, Andrew Saykin, Li Shen

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

1 Scopus citations

Abstract

Identification of biomarkers for early detection of Alzheimer's disease (AD) is an important research topic. Prior work has shown that multimodal imaging and biomarker data could provide complementary information for prediction of cognitive or AD status. However, the relationship among multiple data modalities are often ignored or oversimplified in prior studies. To address this issue, we propose a network-guided sparse learning model to embrace the complementary information and inter-relationships between modalities. We apply this model to predict cognitive outcome from imaging and proteomic data, and show that the proposed model not only outperforms traditional ones, but also yields stable multimodal biomarkers across cross-validation trials.

Original languageEnglish (US)
Title of host publication2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages665-668
Number of pages4
ISBN (Electronic)9781467319591
StatePublished - Jul 29 2014
Event2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 - Beijing, China
Duration: Apr 29 2014May 2 2014

Publication series

Name2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014

Other

Other2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
CountryChina
CityBeijing
Period4/29/145/2/14

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Keywords

  • Cognitive outcome
  • Neuroimaging
  • Proteomic biomarker
  • Regression
  • Sparse learning

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

Cite this

Yan, J., Huang, H., Kim, S., Moore, J., Saykin, A., & Shen, L. (2014). Joint identification of imaging and proteomics biomarkers of alzheimer's disease using network-guided sparse learning. In 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 (pp. 665-668). [6867958] (2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014). Institute of Electrical and Electronics Engineers Inc..