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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Yiming Zuo Guoqiang Yu Chi Zhang Ressom, H.W. |
| Copyright Year | 2014 |
| Description | Author affiliation: Dept. of Electr. & Comput. Eng., Virginia Polytech. Inst. & State Univ., Arlington, VA, USA (Guoqiang Yu) || Lombardi Comprehensive Cancer Center, Georgetown Univ., Washington, DC, USA (Yiming Zuo; Chi Zhang; Ressom, H.W.) |
| Abstract | Recent technological advances have enabled the generation of various omic data (e.g., genomics, proteomics, metabolomics and glycomics) in a high-throughput manner. The integration of multi-omic data sets is desirable to unravel the complexity of a biological system. In this paper, we propose a new approach to investigate both inter and intra relationships for multi-omic data sets by using regularized canonical correlation analysis and graphical lasso. The application of this novel approach on real multi-omic data sets helps identify hub proteins and their neighbors that may be missed by typical statistical analysis to serve as biomarker candidates. Also, the integration of data from various cellular components (i.e., proteins, metabolites and glycans) offers the potential to discover more reliable biomarker candidates for complex disease. |
| Starting Page | 214 |
| Ending Page | 217 |
| File Size | 619457 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781479956692 |
| DOI | 10.1109/BIBM.2014.6999157 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-11-02 |
| Publisher Place | UK |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Proteins Metabolomics Correlation regularized canonical correlation analysis Data integration Proteomics Multi-omic data integration graphical lasso Covariance matrices Bioinformatics |
| Content Type | Text |
| Resource Type | Article |
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