An Exploratory Application of Multilayer Networks and Pathway Analysis in Pharmacogenomics

被引:3
|
作者
Milano, Marianna [1 ,2 ]
Agapito, Giuseppe [2 ,3 ]
Cannataro, Mario [2 ,4 ]
机构
[1] Magna Graecia Univ Catanzaro, Dept Expt & Clin Med, I-88100 Catanzaro, Italy
[2] Magna Graecia Univ Catanzaro, Data Analyt Res Ctr, I-88100 Catanzaro, Italy
[3] Magna Graecia Univ Catanzaro, Dept Law Econ & Social Sci, I-88100 Catanzaro, Italy
[4] Magna Graecia Univ Catanzaro, Dept Med & Surg Sci, I-88100 Catanzaro, Italy
关键词
pharmacogenomics; network analysis; multilayer networks; community detection; pathway enrichment analysis; PROTEIN-PROTEIN INTERACTION; DRUG; IDENTIFICATION;
D O I
10.3390/genes14101915
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
Over the years, network analysis has become a promising strategy for analysing complex system, i.e., systems composed of 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 of uncovering complex relationships and identifying key genes to use in pathway enrichment analysis to figure out biological pathways involved in drug response and adverse reactions. In this study, we modelled omics, disease, and drug data together through multilayer network representation. Then, we mined the multilayer network with a community detection algorithm to obtain the 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 by the selected genes. The results show that the genes forming the top community have multiple roles through different pathways.
引用
收藏
页数:14
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