Functional diversity of topological modules in human protein-protein interaction networks

Wang, Huixin; Chu, Hang; Yu, Jian; Zhou, Xuezhong

doi:10.1038/s41598-017-16270-z

Cited by 14 publications

(14 citation statements)

References 58 publications

(72 reference statements)

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“…This suggests a general mechanism underlying the dynamics of different biological systems, which is likely to extend as well to other contexts. Moreover, several studies have recently shown that there are specific patterns in protein interaction networks that can be determined experimentally 50 , 51 , and which could allow us to identify important biological substructures in the network 52 , 53 . This information could be used, together with the model proposed here, to determine the relevance of such patterns and of the complex interplay between the underlying structure of the network and its functional role, as in the present study.…”

Section: Resultsmentioning

confidence: 99%

Concurrence of form and function in developing networks and its role in synaptic pruning

et al. 2018

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A fundamental question in neuroscience is how structure and function of neural systems are related. We study this interplay by combining a familiar auto-associative neural network with an evolving mechanism for the birth and death of synapses. A feedback loop then arises leading to two qualitatively different types of behaviour. In one, the network structure becomes heterogeneous and dissasortative, and the system displays good memory performance; furthermore, the structure is optimised for the particular memory patterns stored during the process. In the other, the structure remains homogeneous and incapable of pattern retrieval. These findings provide an inspiring picture of brain structure and dynamics that is compatible with experimental results on early brain development, and may help to explain synaptic pruning. Other evolving networks—such as those of protein interactions—might share the basic ingredients for this feedback loop and other questions, and indeed many of their structural features are as predicted by our model.

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Section: Resultsmentioning

confidence: 99%

Concurrence of form and function in developing networks and its role in synaptic pruning

et al. 2018

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“…A recent study of human proteome [35] discussed how most of the topological modules are functionally diverse despite high homogeneity values. In our study, we further this observation by including functional interactions and incorporating the weights to PPIN.…”

Section: Functional Homogeneity and Specificity Of Topological Modulesmentioning

confidence: 99%

“…This leads to the inability to detect smaller modules in a given network. To study the effect of resolution limit in detecting topological modules, we also implemented the Incremental Louvain algorithm [35], which first finds modules by maximizing modularity while incrementally modularizing larger modules into smaller sub-networks, thus converging the algorithm for modules with size greater than a threshold size.…”

Section: Effect Of the Resolution Limit On Module Detection In Ppinmentioning

confidence: 99%

Functional homogeneity and specificity of topological modules in human proteome

Kaalia

Rajapakse

2019

BMC Bioinformatics

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Background: Functional modules in protein-protein interaction networks (PPIN) are defined by maximal sets of functionally associated proteins and are vital to understanding cellular mechanisms and identifying disease associated proteins. Topological modules of the human proteome have been shown to be related to functional modules of PPIN. However, the effects of the weights of interactions between protein pairs and the integration of physical (direct) interactions with functional (indirect expression-based) interactions have not been investigated in the detection of functional modules of the human proteome. Results: We investigated functional homogeneity and specificity of topological modules of the human proteome and validated them with known biological and disease pathways. Specifically, we determined the effects on functional homogeneity and heterogeneity of topological modules (i) with both physical and functional protein-protein interactions; and (ii) with incorporation of functional similarities between proteins as weights of interactions. With functional enrichment analyses and a novel measure for functional specificity, we evaluated functional relevance and specificity of topological modules of the human proteome. Conclusions: The topological modules ranked using specificity scores show high enrichment with gene sets of known functions. Physical interactions in PPIN contribute to high specificity of the topological modules of the human proteome whereas functional interactions contribute to high homogeneity of the modules. Weighted networks result in more number of topological modules but did not affect their functional propensity. Modules of human proteome are more homogeneous for molecular functions than biological processes.

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“…Compared with the previous modularity analysis section where biclustering method is mainly applied to expression data, biclustering takes networks as input in decomposition. Decomposition reduces network complexity and facilitates the exploration of the underlying molecular mechanisms [108][109][110]. Henriques and Madeira [37] developed and applied a pattern-based biclustering algorithm to discover coherent modules from PPI and showed that most modules were significantly enriched with particular biological functions.…”

Section: Biological Network Elucidationmentioning

confidence: 99%

It is time to apply biclustering: a comprehensive review of biclustering applications in biological and biomedical data

Xie

Fennell

et al. 2018

Briefings in Bioinformatics

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Biclustering is a powerful data mining technique that allows clustering of rows and columns, simultaneously, in a matrix-format data set. It was first applied to gene expression data in 2000, aiming to identify co-expressed genes under a subset of all the conditions/samples. During the past 17 years, tens of biclustering algorithms and tools have been developed to enhance the ability to make sense out of large data sets generated in the wake of high-throughput omics technologies. These algorithms and tools have been applied to a wide variety of data types, including but not limited to, genomes, transcriptomes, exomes, epigenomes, phenomes and pharmacogenomes. However, there is still a considerable gap between biclustering methodology development and comprehensive data interpretation, mainly because of the lack of knowledge for the selection of appropriate biclustering tools and further supporting computational techniques in specific studies. Here, we first deliver a brief introduction to the existing biclustering algorithms and tools in public domain, and then systematically summarize the basic applications of biclustering for biological data and more advanced applications of biclustering for biomedical data. This review will assist researchers to effectively analyze their big data and generate valuable biological knowledge and novel insights with higher efficiency.

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Functional diversity of topological modules in human protein-protein interaction networks

Cited by 14 publications

References 58 publications

Concurrence of form and function in developing networks and its role in synaptic pruning

Concurrence of form and function in developing networks and its role in synaptic pruning

Functional homogeneity and specificity of topological modules in human proteome

It is time to apply biclustering: a comprehensive review of biclustering applications in biological and biomedical data

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