Complexity of cascading interrelations between molecular cell components at different levels fromgenome to metabolome ordains a massive difficulty in comprehending biological happenings. However, considering these complications in the systematic modelings will result in realistic and reliable outputs. The multilayer networks approach is a relatively innovative concept that could be applied for multiple omics datasets as an integrative methodology to overcome heterogeneity difficulties. Herein, we employed the multilayer framework to rehabilitate colon adenocarcinoma network by observing co-expression correlations, regulatory relations, and physical binding interactions. Hub nodes in this three-layer network were selected using a heterogeneous random walk with random jump procedure. We exploited local composite modules around the hub nodes having high overlay with cancer-specific pathways, and investigated their genes showing a different expressional pattern in the tumor progression. These genes were examined for survival effects on the patient's lifespan, and those with significant impacts were selected as potential candidate biomarkers. Results suggest that identified genes indicate noteworthy importance in the carcinogenesis of the colon. open Scientific RepoRtS | (2020) 10:4991 | https://doi.org/10.1038/s41598-020-59605-z www.nature.com/scientificreports www.nature.com/scientificreports/ to explore it. Second, Ensemble (Consensus) approaches, in which each layer is individually evaluated; then, the results are combined to create the final consequence. Third, methods extended for multilayer networks (briefly called extended approaches), in which the analysis process is simultaneously conducted on all layers. Didier et al. 13 compared these three approaches in terms of community detection and found that the extended modularity function has superiority over the other two methods.The extension of topological attributes from monolayer to multilayer is a critical and challenging topic in this area 12,14-16 . Hmimida et al. 12 have defined metrics (such as degree, shortest-path, neighbor set) for multiplex networks using an entropy-like aggregate function. Domenico et al. 16 proposed reducibility methods for multilayer networks to eliminate redundant interactions and layers. In this context, community detection for multilayer networks is considered one of the most challenging topics. Given the topological perspective, a community is a cluster of densely connected nodes, which are far from other clusters. Communities may be either local or global and may have overlap with each other. Recently, various extended multilayer community detection algorithms have been proposed to seek modules in layers simultaneously [17][18][19][20][21] . A specific type of community detection method is based on seed-centric approach, in which communities are localized around predefined (manual or computational) seed nodes 12,22 .Extended approaches for multilayer networks were recently used in biological and medical sciences. Berenstein et...

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Background: Complexity and dynamicity of biological events is a reason to use comprehensive and holistic approaches to deal with their difficulty. Currently with advances in omics data generation, network-based approaches are used frequently in different areas of computational biology and bioinformatics to solve problems in a systematic way. Also, there are many applications and tools for network data analysis and manipulation which their goal is to facilitate the way of improving our understandings of inter/intra cellular interactions. Methods: In this article, we introduce CatbNet, a multi network analyzer application which is prepared for network comparison objectives. Result and Conclusion: CatbNet uses many topological features of networks to compare their structure and foundations. One of the most prominent properties of this application is classified network analysis in which groups of networks are compared with each other.

Regardless of all efforts on community discovery algorithms, it is still an open and challenging subject in network science. Recognizing communities in a multilayer network, where there are several layers (types) of connections, is even more complicated. Here, we concentrated on a specific type of communities called seed-centric local communities in the multilayer environment and developed a novel method based on the information cascade concept, called PLCDM. Our simulations on three datasets (real and artificial) signify that the suggested method outstrips two known earlier seed-centric local methods. Additionally, we compared it with other global multilayer and single-layer methods. Eventually, we applied our method on a biological two-layer network of Colon Adenocarcinoma (COAD), reconstructed from transcriptomic and post-transcriptomic datasets, and assessed the output modules. The functional enrichment consequences infer that the modules of interest hold biomolecules involved in the pathways associated with the carcinogenesis.