PUBHL Biostatistics Division
School of Public Health
Twin Cities
School of Public Health
Twin Cities
Project Title:
Development of Innovative Bayesian Methods for -Omics Data Integration Analysis
This group aims at developing innovative statistical methods for multi-omics data integration analysis. They evaluate the performance of their methods through simulation studies. They have used techniques such as variable selection, biclustering, functional data analysis, and dimension reduction (e.g., Principal Component analysis). They used the Markov chain Monte Carlo algorithm for the estimation of their parameter in the model.
Project Investigators
Dr. Thierry Chekouo Tekougang
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