Application of Bayesian networks for inferring cause–effect relations from gene expression profiles of cancer versus normal cells Andrzej Polanski a , Joanna Polanska b, * , Michal Jarzab c , Malgorzata Wiench c , Barbara Jarzab c a Department of Computer Science, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland b System Engineering Group, Department of Automatic Control, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland c Department of Nuclear Medicine and Endocrine Oncology, Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Gliwice Branch, Wybrzeze Armii Krajowej 15, 44-101 Gliwice, Poland Received 21 September 2005; received in revised form 15 December 2006; accepted 9 March 2007 Available online 27 March 2007 Abstract The paper is devoted to two questions: whether distinction of causes versus effects of neoplasia leaves a signature in the cancer versus normal gene expression profiles and whether roles of genes, ‘‘causes’’ or ‘‘effects’’, can be inferred from repeated measurements of gene expressions. We model joint probability dis- tributions of logarithms of gene expressions with the use of Bayesian networks (BN). Fitting our models to real data confirms that our BN models have the ability to explain some aspects of observational evidence from DNA microarray experiments. Effects of neoplastic transformation are well seen among genes with the highest power to differentiate between normal and cancer cells. Likelihoods of BNs depend on the bio- logical role of selected genes, defined by Gene Ontology. Also predictions of our BN models are coherent with the set of putative causes and effects constructed based on our data set of papillary thyroid cancer. Ó 2007 Elsevier Inc. All rights reserved. Keywords: Applied statistics; Cancer; DNA microarrays; Cause–effect relations; Bayesian networks 0025-5564/$ - see front matter Ó 2007 Elsevier Inc. All rights reserved. doi:10.1016/j.mbs.2007.03.006 * Corresponding author. Tel.: +48 32 2372144; fax:+48 32 2371921. E-mail address: jkp@stat.rice.edu (J. Polanska). www.elsevier.com/locate/mbs Mathematical Biosciences 209 (2007) 528–546