RETRACTED ARTICLE: Analysis of protein-protein interaction network and functional modules on primary osteoporosis

Li, Gai‐Li; Xu, Xianhua; Wang, Bing-Ang; Yao, Yi-Min; Yang, Qin; Bai, Shan; Rong, Jian; Deng, Tao; Hu, Yonghe

doi:10.1186/2047-783x-19-15

Cited by 5 publications

(4 citation statements)

References 29 publications

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“…In addition to shedding light on why biological networks evolve to be hierarchical, this work also lends additional support to the hypothesis that a connection cost may also drive the emergence of hierarchy in human-constructed networks, such as company organizations [41], road systems [53], and the Internet [9]. Furthermore, knowing how to evolve hierarchical networks can improve medical research [54,55], which benefits from more biologically realistic, and thus hierarchical, network models [56,57].…”

Section: Discussionmentioning

confidence: 53%

The Evolutionary Origins of Hierarchy

et al. 2016

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Hierarchical organization—the recursive composition of sub-modules—is ubiquitous in biological networks, including neural, metabolic, ecological, and genetic regulatory networks, and in human-made systems, such as large organizations and the Internet. To date, most research on hierarchy in networks has been limited to quantifying this property. However, an open, important question in evolutionary biology is why hierarchical organization evolves in the first place. It has recently been shown that modularity evolves because of the presence of a cost for network connections. Here we investigate whether such connection costs also tend to cause a hierarchical organization of such modules. In computational simulations, we find that networks without a connection cost do not evolve to be hierarchical, even when the task has a hierarchical structure. However, with a connection cost, networks evolve to be both modular and hierarchical, and these networks exhibit higher overall performance and evolvability (i.e. faster adaptation to new environments). Additional analyses confirm that hierarchy independently improves adaptability after controlling for modularity. Overall, our results suggest that the same force–the cost of connections–promotes the evolution of both hierarchy and modularity, and that these properties are important drivers of network performance and adaptability. In addition to shedding light on the emergence of hierarchy across the many domains in which it appears, these findings will also accelerate future research into evolving more complex, intelligent computational brains in the fields of artificial intelligence and robotics.

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Section: Discussionmentioning

confidence: 53%

The Evolutionary Origins of Hierarchy

et al. 2016

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“…High aggregation is an essential characteristic of biological systems, and can be visualized in the form of high modularization in PPI networks (Li et al, 2014). Therefore, in the context of high complexity of a PPI network of any given organism, a large number of protein clusters (subnetworks) is expected.…”

Section: Structural and Functional Modularity Within The Enolase Ppi mentioning

confidence: 99%

Systems biology approach reveals possible evolutionarily conserved moonlighting functions for enolase

Paludo

Lorenzatto

Bonatto

et al. 2015

Computational Biology and Chemistry

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“…The Affy package with the R package (version 3.5.1) was used to preprocess the original data from the four datasets, including quantile normalization, log2 transformation, and the K-nearest neighbors algorithm to supplement missing values [11] . The Limma package in R was used to identify DEGs in the high BMD and low BMD groups [12] .…”

Section: Data Preprocessing and Analysis Of Degsmentioning

confidence: 99%

Identification of Candidate Genes for Osteoporosis via Integrated Bioinformatics Analysis

Xie¹,

Cao²,

Ye³

et al. 2021

ijps

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Xie et al.: Identification of Candidate Genes for OsteoporosisOsteoporosis is a common bone disease; however, its pathophysiology is yet unclear. This study aimed to investigate candidate genes in osteoporosis and its pathomechanism. Microarray datasets Genomic Spatial Event-7429, 13850, 56815, and 7158, comprising data obtained from blood samples of osteoporosis patients and healthy controls, were downloaded from the Gene Expression Omnibus database. Differentially expressed genes were identified via intersection of the four datasets, using Affy and Limma software packages. Functional and pathway enrichment analysis of differentially expressed genes were conducted using the database for annotation, visualization, and integrated discovery database. Thereafter, proteinprotein interactions between the products of differentially expressed genes and key modules were analyzed using the search tool for the retrieval of interacting genes/proteins database and Cytoscape software. Furthermore, a transcriptional regulatory network was established with the differentially expressed genes, using the Web-based gene set analysis toolkit database. In total, 702 differentially expressed genes were filtered from the intersection of the four datasets, which were primarily enriched in functions and pathways associated with glutamate secretion, Adenosine triphosphate binding, extracellular region, and phosphatidylinositol 3-kinase-protein kinase B signaling pathway. Furthermore, 361 nodes and 846 edges were present in the protein-protein interaction network. Two significant modules were obtained, each containing 22 key genes. Transcriptional factor-target gene regulatory network analysis revealed two vital transcription factors, Forkhead Box O4 and LIM Homeobox 3. Genes including endothelin receptor type A, XC chemokine receptor 1, 5-hydroxytryptamine receptor 2a, phosphatidylinositol-4,5bisphosphate phosphodiesterase beta-4, glutamate metabotropic receptor 5, kiss-1 metastasis suppressor, 1-phosphatidylinositol-4,5-bisphosphate phosphodiesterase beta-2, cholinergic receptor muscarinic 3, g protein subunit gamma 4, neurotensin receptor 1, neuromedin U, neuromedin B, breast cancer gene 1 associated RING domain 1, exonuclease 1, replication factor C subunit 2, restriction site associated DNA 52 homolog DNA repair protein, DNA topoisomerase III alpha, Hemolytic uremic syndrome 1 checkpoint clamp component, timeless circadian regulator, breast cancer gene 1 DNA repair associated, tumor protein p53 and claspin are potential key genes for osteoporosis and may help elucidate the underlying pathomechanism, and transcription factors Forkhead Box O4 and LIM Homeobox 3 are potential therapeutic targets for osteoporosis.

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RETRACTED ARTICLE: Analysis of protein-protein interaction network and functional modules on primary osteoporosis

Cited by 5 publications

References 29 publications

The Evolutionary Origins of Hierarchy

The Evolutionary Origins of Hierarchy

Systems biology approach reveals possible evolutionarily conserved moonlighting functions for enolase

Identification of Candidate Genes for Osteoporosis via Integrated Bioinformatics Analysis

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