A Bayesian network approach incorporating imputation of missing data enables exploratory analysis of complex causal biological relationships.

ABSTRACT: Bayesian networks can be used to identify possible causal relationships between variables based on their conditional dependencies and independencies, which can be particularly useful in complex biological scenarios with many measured variables. Here we propose two improvements to an existing method for Bayesian network analysis, designed to increase the power to detect potential causal relationships between variables (including potentially a mixture of both discrete and continuous variables). Our first improvement relates to the treatment of missing data. When there is missing data, the standard approach is to remove every individual with any missing data before performing analysis. This can be wasteful and undesirable when there are many individuals with missing data, perhaps with only on

SUBMITTER: Howey R 

PROVIDER: S-EPMC8504979 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature