TY - GEN
T1 - Biomarker identification for diagnosis of schizophrenia with integrated analysis of fMRI and SNPs
AU - Cao, Hongbao
AU - Lin, Dongdong
AU - Duan, Junbo
AU - Wang, Yu Ping
AU - Calhoun, Vince
PY - 2012/12/1
Y1 - 2012/12/1
N2 - It is important to identify significant biomarkers such as SNPs for medical diagnosis and treatment. However, the size of a biological sample is usually far less than the number of measurements, which makes the problem more challenging. To overcome this difficulty, we propose a sparse representation based variable selection (SRVS) approach. A simulated data set was first tested to demonstrate the advantages and properties of the proposed method. Then, we applied the algorithm to a joint analysis of 759075 SNPs and 153594 functional magnetic resonance imaging (fMRJ) voxels in 208 subjects (92 cases/116 controls) to identify significant biomarkers for schizophrenia (SZ). When compared with previous studies, our proposed method located 20 genes out of the top 45 SZ genes that are publicly reported We also detected some interesting functional brain regions from the fMRI study. In addition, a leave one out (LOO) cross-validation was performed and the results were compared with that of a previously reported method, which showed that our method gave significantly higher classification accuracy. In addition, the identification accuracy with integrative analysis is much better than that of using single type of data, suggesting that integrative analysis may lead to better diagnostic accuracy by combining complementary SNP and fMRI data.
AB - It is important to identify significant biomarkers such as SNPs for medical diagnosis and treatment. However, the size of a biological sample is usually far less than the number of measurements, which makes the problem more challenging. To overcome this difficulty, we propose a sparse representation based variable selection (SRVS) approach. A simulated data set was first tested to demonstrate the advantages and properties of the proposed method. Then, we applied the algorithm to a joint analysis of 759075 SNPs and 153594 functional magnetic resonance imaging (fMRJ) voxels in 208 subjects (92 cases/116 controls) to identify significant biomarkers for schizophrenia (SZ). When compared with previous studies, our proposed method located 20 genes out of the top 45 SZ genes that are publicly reported We also detected some interesting functional brain regions from the fMRI study. In addition, a leave one out (LOO) cross-validation was performed and the results were compared with that of a previously reported method, which showed that our method gave significantly higher classification accuracy. In addition, the identification accuracy with integrative analysis is much better than that of using single type of data, suggesting that integrative analysis may lead to better diagnostic accuracy by combining complementary SNP and fMRI data.
KW - Integrated analysis
KW - SNP
KW - Sparse representations
KW - Variable selection
KW - fMRI
UR - http://www.scopus.com/inward/record.url?scp=84872540815&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84872540815&partnerID=8YFLogxK
U2 - 10.1109/BIBM.2012.6392674
DO - 10.1109/BIBM.2012.6392674
M3 - Conference contribution
AN - SCOPUS:84872540815
SN - 9781467325585
T3 - Proceedings - 2012 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2012
SP - 223
EP - 228
BT - Proceedings - 2012 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2012
T2 - 2012 IEEE International Conference on Bioinformatics and Biomedicine, BIBM2012
Y2 - 4 October 2012 through 7 October 2012
ER -