2010/02/10 by Jessica Kasza, Kasza, Jessica, Gary Glonek +2
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Gene expression and cancer classification #Methodology (stat.ME) #Statistical Methods and Bayesian Inference #Statistical Methods and Inference
paper · pdf · doi:10.48550/arxiv.1002.2168
openalex publication_date 2010/02/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The estimation of Bayesian networks given high-dimensional data, in\nparticular gene expression data, has been the focus of much recent research.\nWhilst there are several methods available for the estimation of such networks,\nthese typically assume that the data consist of independent and identically\ndistributed samples. However, it is often the case that the available data have\na more complex mean structure plus additional components of variance, which\nmust then be accounted for in the estimation of a Bayesian network. In this\npaper, score metrics that take account of such complexities are proposed for\nuse in conjunction with score-based methods for the estimation of Bayesian\nnetworks. We propose firstly, a fully Bayesian score metric, and secondly, a\nmetric inspired by the notion of restricted maximum likelihood. We demonstrate\nthe performance of these new metrics for the estimation of Bayesian networks\nusing simulated data with known complex mean structures. We then present the\nanalysis of expression levels of grape berry genes adjusting for exogenous\nvariables believed to affect the expression levels of the genes. Demonstrable\nbiological effects can be inferred from the estimated conditional independence\nrelationships and correlations amongst the grape-berry genes.\n