TY - GEN
T1 - CCA based multi-view feature selection for multiomics data integration
AU - El-Manzalawy, Yasser
PY - 2018/7/5
Y1 - 2018/7/5
N2 - Recent technological advances in high-throughput omics technologies and their applications in genomic medicine have opened up outstanding opportunities for individualized medicine. However, several challenges arise in the integrative analysis of such data including heterogeneity and high dimensionality of the omics data. In this study, we present a novel multi-view feature selection algorithm based on the wellknown canonical correlation analysis (CCA) statistical method for jointly selecting discriminative features from multi-omics data sources (multi-views). Our results demonstrate that models for predicting kidney renal clear cell carcinoma (KIRC) survival using our proposed method for jointly selecting discriminative features from copy number alteration (CNA), gene expression RNA-Seq, and reverse-phase protein arrays (RPPA) views outperform models trained using single-view data as well as three integrated models developed using data fusion approaches including CCA-based feature fusion.
AB - Recent technological advances in high-throughput omics technologies and their applications in genomic medicine have opened up outstanding opportunities for individualized medicine. However, several challenges arise in the integrative analysis of such data including heterogeneity and high dimensionality of the omics data. In this study, we present a novel multi-view feature selection algorithm based on the wellknown canonical correlation analysis (CCA) statistical method for jointly selecting discriminative features from multi-omics data sources (multi-views). Our results demonstrate that models for predicting kidney renal clear cell carcinoma (KIRC) survival using our proposed method for jointly selecting discriminative features from copy number alteration (CNA), gene expression RNA-Seq, and reverse-phase protein arrays (RPPA) views outperform models trained using single-view data as well as three integrated models developed using data fusion approaches including CCA-based feature fusion.
UR - http://www.scopus.com/inward/record.url?scp=85051017772&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85051017772&partnerID=8YFLogxK
U2 - 10.1109/CIBCB.2018.8404968
DO - 10.1109/CIBCB.2018.8404968
M3 - Conference contribution
AN - SCOPUS:85051017772
T3 - 2018 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2018
SP - 1
EP - 8
BT - 2018 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2018
Y2 - 30 May 2018 through 2 June 2018
ER -