Bayesian Computation Methods for Inferring Regulatory Network Models Using Biomedical Data.
The rapid advancement of high-throughput technologies provides huge amounts of information for gene expression and protein activity in the genome-wide scale. The availability of genomics, transcriptomics, proteomics, and metabolomics dataset gives an unprecedented opportunity to study detailed molecular regulations that is very important to precision medicine. However, it is still a significant challenge to design effective and efficient method to infer the network structure and dynamic property of regulatory networks. In recent years a number of computing methods have been designed to explore the regulatory mechanisms as well as estimate unknown model parameters. Among them, the Bayesian inference method can combine both prior knowledge and experimental data to generate updated information regarding the regulatory mechanisms. This chapter gives a brief review for Bayesian statistical methods that are used to infer the network structure and estimate model parameters based on experimental data.
School of Mathematical Science, Monash University, Clayton, VIC, 3800, Australia. tianhai.tian@monash.edu.
Tianhai
T
Tian
D000465:Algorithms; D001499:Bayes Theorem; D019295:Computational Biology / Q000706:statistics & numerical data*; D053263:Gene Regulatory Networks; D015894:Genome, Human; D006801:Humans; D020928:Mitogen-Activated Protein Kinases / Q000235:genetics*; D008957:Models, Genetic; D009010:Monte Carlo Method; D057285:Precision Medicine
D020928:Mitogen-Activated Protein Kinases
Approximate Bayesian computation; Bayesian inference; Genetic regulation; Reverse engineering