Milano, M.; Agapito, G.; Cannataro, M. An Exploratory Application of Multilayer Networks and Pathway Analysis in Pharmacogenomics. Genes2023, 14, 1915.
Milano, M.; Agapito, G.; Cannataro, M. An Exploratory Application of Multilayer Networks and Pathway Analysis in Pharmacogenomics. Genes 2023, 14, 1915.
Milano, M.; Agapito, G.; Cannataro, M. An Exploratory Application of Multilayer Networks and Pathway Analysis in Pharmacogenomics. Genes2023, 14, 1915.
Milano, M.; Agapito, G.; Cannataro, M. An Exploratory Application of Multilayer Networks and Pathway Analysis in Pharmacogenomics. Genes 2023, 14, 1915.
Abstract
Over the years, network analysis became a promising strategy to analyze complex system, i.e. systems composed by a large number of interacting elements. In particular, multilayer networks have emerged as a powerful framework for modelling and analysing complex systems with multiple types of interactions. Network analysis can be applied to pharmacogenomics to gain insights into the interactions between genes, drugs, and diseases. By integrating network analysis techniques with pharmacogenomic data, the goal consists to uncover complex relationships and identify key genes to use in pathway enrichment analysis to figure out biological pathways involved in drug response and adverse reactions. In this study, we model omics, diseases and drugs data together through the multilayer network representation. Then, we mined the multilayer network with a community detection algorithm by obtaining top communities. After that, we used the identified list of genes from the communities to perform pathway enrichment analysis (PEA) to figure out the biological function affected form the selected genes. The results show that the genes forming the top community have the multiple roles through different pathways.
Keywords
pharmacogenomics; network analysis; multilayer networks; community detection; pathway enrichment analysis
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
