Assessing edge-coupled interdependent network disintegration via rank aggregation and elite enumeration

The disintegration of networks is a widely researched topic with significant applications in fields such as counterterrorism and infectious disease control. While the traditional approaches for achieving network disintegration involve identifying critical sets of nodes or edges, limited research has been carried out on edge-based disintegration strategies. We propose a novel algorithm, i.e., a rank aggregation elite enumeration algorithm based on edge-coupled networks(RAEEC),which aims to implement tiling for edge-coupled networks by finding important sets of edges in the network while balancing effectiveness and efficiency. Our algorithm is based on a two-layer edge-coupled network model with one-to-one links, and utilizes three advanced edge importance metrics to rank the edges separately. A comprehensive ranking of edges is obtained using a rank aggregation approach proposed in this study. The top few edges from the ranking set obtained by RAEEC are then used to generate an enumeration set, which is continuously iteratively updated to identify the set of elite attack edges.We conduct extensive experiments on synthetic networks to evaluate the performance of our proposed method, and the results indicate that RAEEC achieves a satisfactory balance between efficiency and effectiveness. Our approach represents a significant contribution to the field of network disintegration, particularly for edge-based strategies.

Identification of Hub Genes Associated with Hepatocellular Carcinoma Prognosis by Bioinformatics Analysis

Objective: This study aimed to identify hub genes that are associated with hepatocellular carcinoma (HCC) prognosis by bioinformatics analysis. Methods: Data were collected from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) liver HCC datasets. The robust rank aggregation algorithm was used in integrating the data on differentially expressed genes (DEGs). Online databases DAVID 6.8 and REACTOME were used for gene ontology and pathway enrichment analysis. R software version 3.5.1, Cytoscape, and Kaplan-Meier plotter were used to identify hub genes. Results: Six GEO datasets and the TCGA liver HCC dataset were included in this analysis. A total of 151 upregulated and 245 downregulated DEGs were identified. The upregulated DEGs most significantly enriched in the functional categories of cell division, chromosomes, centromeric regions, and protein binding, whereas the downregulated DEGs most significantly enriched in the epoxygenase P450 pathway, extracellular region, and heme binding, with respect to biological process, cellular component, and molecular function analysis, respectively. Upregulated DEGS most significantly enriched the cell cycle pathway, whereas downregulated DEGs most significantly enriched the metabolism pathway. Finally, 88 upregulated and 40 downregulated genes were identified as hub genes. The top 10 upregulated hub DEGs were CDK1, CCNB1, CCNB2, CDC20, CCNA2, AURKA, MAD2L1, TOP2A, BUB1B and BUB1. The top 10 downregulated hub DEGs were ESR1, IGF1, FTCD, CYP3A4, SPP2, C8A, CYP2E1, TAT, F9 and CYP2C9. Conclusions: This study identified several upregulated and downregulated hub genes that are associated with the prognosis of HCC patients. Verification of these results using in vitro and in vivo studies is warranted.