Restricted-derestricted dynamic Bayesian Network inference of transcriptional regulatory relationships among genes in cancer.

Autor: Adabor ES; School of Technology, Ghana Institute of Management and Public Administration, Achimota, Accra, Ghana. Electronic address: emmanuelsadabor@gimpa.edu.gh., Acquaah-Mensah GK; Pharmaceutical Sciences Department, Massachusetts College of Pharmacy and Health Sciences (MCPHS University), 19 Foster Street, Worcester, MA, USA.
Jazyk: angličtina
Zdroj: Computational biology and chemistry [Comput Biol Chem] 2019 Apr; Vol. 79, pp. 155-164. Date of Electronic Publication: 2019 Feb 21.
DOI: 10.1016/j.compbiolchem.2019.02.006
Abstrakt: Understanding transcriptional regulatory relationships among genes is important for gaining etiological insights into diseases such as cancer. To this end, high-throughput biological data have been generated through advancements in a variety of technologies. These rely on computational approaches to discover underlying structures in such data. Among these computational approaches, Bayesian networks (BNs) stand out because their probabilistic nature enables them to manage randomness in the dynamics of gene regulation and experimental data. Feedback loops inherent in networks of regulatory relationships are more tractable when enhancements to BNs are applied to them. Here, we propose Restricted-Derestricted dynamic BNs with a novel search technique, Restricted-Derestricted Greedy Method, for such tasks. This approach relies on the Restricted-Derestricted Greedy search technique to infer transcriptional regulatory networks in two phases: restricted inference and derestricted inference. An application of this approach to real data sets reveals it performs favourably well compared to other existing well performing dynamic BN approaches in terms of recovering true relationships among genes. In addition, it provides a balance between searching for optimal networks and keeping biologically relevant regulatory interactions among variables.
(Copyright © 2019 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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