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

Bayesian network analysis incorporating genetic anchors complements conventional Mendelian randomization approaches for exploratory analysis of causal relationships in complex data

(2020) Richard Howey et al.   PLoS Genetics

A comparison of methods for inferring causal relationships between genotype and phenotype using additional biological measurements

(2017) Holly F. Ainsworth et al.   GENETIC EPIDEMIOLOGY

Untangling the role of one-carbon metabolism in colorectal cancer risk: a comprehensive Bayesian network analysis

(2017) Robin Myte et al.   Scientific Reports

Nearest neighbor imputation algorithms: a critical evaluation

(2016) Lorenzo Beretta et al.   BMC Medical Informatics and Decision Making

Bayesian network data imputation with application to survival tree analysis

(2016) Paola M.V. Rancoita et al.   COMPUTATIONAL STATISTICS & DATA ANALYSIS

Identifying significant edges in graphical models of molecular networks

(2013) Marco Scutari et al.   ARTIFICIAL INTELLIGENCE IN MEDICINE

A missing value imputation method using a Bayesian network with weighted learning

(2012) Yoshihiro Miyakoshi et al.   Electronics and Communications in Japan

Multiple imputation by chained equations: what is it and how does it work?

(2011) Melissa J. Azur et al.   INTERNATIONAL JOURNAL OF METHODS IN PSYCHIATRIC RESEARCH