@inproceedings{f2ea53d751764548af35dd6fca2425d0,
title = "Sparse representation based biomarker selection for schizophrenia with integrated analysis of fMRI and SNP data",
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.",
keywords = "SNP, Sparse representations, Variable selection, fMRI, schizophrenia",
author = "Hongbao Cao and Junbo Duan and Dongdong Lin and Vince Calhoun and Wang, \{Yu Ping\}",
year = "2013",
doi = "10.1109/ISBI.2013.6556585",
language = "Ingl{\'e}s",
isbn = "9781467364546",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE Computer Society",
pages = "756--759",
booktitle = "ISBI 2013 - 2013 IEEE 10th International Symposium on Biomedical Imaging",
address = "Estados Unidos",
note = "10th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2013 ; Conference date: 07-04-2013 Through 11-04-2013",
}