Statistical Modeling of OMIC Data
| Category | Teaching Materials |
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| Abstract | This presentation describes the statistical modeling work of Professor Min Zhang. A discussion of statistical methods for identifying biomarkers is presented first, including Classical methods and Bayesian and Regularized Variable Selection methods. Next, the alignment of GCxGC MS data using the two-dimensional Correlation Optimized Warping (2D-COW) algorithm is described in detail. Finally, future efforts in the processing and analysis of OMIC data are identified.
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| Contributor | Ann Christine Catlin
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| credits | Professor Min Zhang, Statistics Department, Purdue University |
| Cite this work | Researchers should cite this work as follows: |
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