Mining Functional Modules in Heterogeneous Biological Networks Using Multiplex PageRank Approach

Li, Jun; Zhao, Patrick Xuechun

doi:10.3389/fpls.2016.00903

Cited by 15 publications

(13 citation statements)

References 57 publications

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“…To overcome this hurdle, various approaches have been developed to reveal the fundamental mechanisms that control dynamic cell organization by analyzing biological networks [ 7 8 9 ]. However, for efficient biological network analyses, traditional relational database systems, such as MySQL and Oracle, may be limited, because traditional relational databases store data in multiple tables and then infer relationships by applying multiple-join statements.…”

Section: Introductionmentioning

confidence: 99%

Use of Graph Database for the Integration of Heterogeneous Biological Data

Yoon

Kim

2017

Genomics Inform

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Understanding complex relationships among heterogeneous biological data is one of the fundamental goals in biology. In most cases, diverse biological data are stored in relational databases, such as MySQL and Oracle, which store data in multiple tables and then infer relationships by multiple-join statements. Recently, a new type of database, called the graph-based database, was developed to natively represent various kinds of complex relationships, and it is widely used among computer science communities and IT industries. Here, we demonstrate the feasibility of using a graph-based database for complex biological relationships by comparing the performance between MySQL and Neo4j, one of the most widely used graph databases. We collected various biological data (protein-protein interaction, drug-target, gene-disease, etc.) from several existing sources, removed duplicate and redundant data, and finally constructed a graph database containing 114,550 nodes and 82,674,321 relationships. When we tested the query execution performance of MySQL versus Neo4j, we found that Neo4j outperformed MySQL in all cases. While Neo4j exhibited a very fast response for various queries, MySQL exhibited latent or unfinished responses for complex queries with multiple-join statements. These results show that using graph-based databases, such as Neo4j, is an efficient way to store complex biological relationships. Moreover, querying a graph database in diverse ways has the potential to reveal novel relationships among heterogeneous biological data.

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Section: Introductionmentioning

confidence: 99%

Use of Graph Database for the Integration of Heterogeneous Biological Data

Yoon

Kim

2017

Genomics Inform

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“…The density-based method allows a protein to repeatedly emerge in the process of expanding the search, which enables achieving the goal that the same protein belongs to different clusters, but this method cannot identify the nondense sub-graph structure in the protein network [22]. In the actual protein network, each protein node may belong to a number of protein complexes or functional modules, with multiple functions, involved in a number of different biological processes [23].…”

Section: Discussionmentioning

confidence: 99%

Protein–protein interaction networks and different clustering analysis in Burkitt’s lymphoma

et al. 2017

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“…First, to assess our results in contrast with other multilayer methods, two well-known frameworks, Gene4x 46 and mPageRank 19 , were selected and measures of central tendency were used to examine the distribution of each method overlap with disease-related genes ( Table 6). We applied the mPageRank on the two layers of co-expression and PPI, by choosing random seeds from COAD-specific genes, as defined in the original article.…”

Section: Methods Evaluationmentioning

confidence: 99%

“…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.…”

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confidence: 99%

Identification of Key Components in Colon Adenocarcinoma Using Transcriptome to Interactome Multilayer Framework

Pournoor

Mousavian

Dalini

et al. 2020

Sci Rep

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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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Mining Functional Modules in Heterogeneous Biological Networks Using Multiplex PageRank Approach

Cited by 15 publications

References 57 publications

Use of Graph Database for the Integration of Heterogeneous Biological Data

Use of Graph Database for the Integration of Heterogeneous Biological Data

Protein–protein interaction networks and different clustering analysis in Burkitt’s lymphoma

Identification of Key Components in Colon Adenocarcinoma Using Transcriptome to Interactome Multilayer Framework

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