Determination of relationships among cancer-related genes using Bayesian networks Online publication date: Wed, 16-Nov-2022
by Michael Kofi Ahenkan; Emmanuel S. Adabor; Kwaku F. Darkwah
International Journal of Computational Biology and Drug Design (IJCBDD), Vol. 15, No. 2, 2022
Abstract: A network of relationships among cancer-related genes can be reconstructed from high-throughput datasets obtained by deoxyribonucleic acid (DNA) micro-array technologies. However, modelling such biological networks is challenged by the nature of data and the complexities of relationships among biological variables such as genes. In this paper, Bayesian networks are applied to predict novel regulatory relationships among genes in cancer from genomic datasets. The performances of the methods were assessed by standard metrics such as sensitivities and specificities. Furthermore, in order to validate and verify the reliability of the new predicted relationships among the genes, some of the results were examined with experimentally confirmed relationships found by previous research. Interestingly, some predicted regulatory relationships were also found in the literature. This enhances confidence in the newly predicted network of regulatory relationships, which could become hypotheses for further research.
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