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Updated: Jul 15, 2026

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
Application of Bayesian networks for inferring cause-effect relations from gene expression profiles of cancer versus
Andrzej Polanski1, Joanna Polanska, Michal Jarzab
1Department of Computer Science, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland.
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 distributions 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 biological 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.
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