Sparse representation based biomarker selection for schizophrenia with integrated analysis of fMRI and SNP data

Hongbao Cao, Junbo Duan, Dongdong Lin, Vince Calhoun, Yu Ping Wang

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

Abstract

We propose a novel sparse representation based variable selection algorithm (SRVS), which improves the variable selection ability of a traditional sparse regression model in that it performs variable selection at different significance levels, and gives groups of selected variables of different sizes. As an example, we applied the algorithm to a joint analysis of 759075 SNPs and 153594 functional magnetic resonance imaging (fMRI) voxels in 208 subjects (92 cases/116 controls) to identify biomarkers for schizophrenia (SZ). To evaluate the selected biomarkers, a 10-fold cross validation was performed. The results between SRVS method and a previously reported variable selection method were compared, which showed that our method, especially with a sparse regression model penalized with L1/2 norm, gave significantly higher classification accuracy of discriminating SZ patients from healthy controls.

Original languageEnglish (US)
Title of host publicationISBI 2013 - 2013 IEEE 10th International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro
Pages756-759
Number of pages4
DOIs
StatePublished - Aug 22 2013
Externally publishedYes
Event2013 IEEE 10th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2013 - San Francisco, CA, United States
Duration: Apr 7 2013Apr 11 2013

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Other

Other2013 IEEE 10th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2013
CountryUnited States
CitySan Francisco, CA
Period4/7/134/11/13

Keywords

  • SNP
  • Sparse representations
  • Variable selection
  • fMRI
  • schizophrenia

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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