Integrated analysis of multiple data sources reveals modular structure of biological networks

Lu, Hongchao; Shi, Baochen; Wu, Guangjian; Zhang, Yong; Zhu, Xiaopeng; Zhang, Zhihua; Liu, Changning; Zhao, Yi; Wu, Tao; Wang, Jie; Chen, Runsheng

doi:10.1016/j.bbrc.2006.04.088

Cited by 34 publications

(37 citation statements)

References 26 publications

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“…They argued that protein complexes are groups of proteins, which have similar biochemical characters, interacting with each other at the same time, and functional modules, by contrast, are groups of proteins participating in a particular cellular process while binding to each other at different times [18]. Lu et al [16] integrated protein interaction data with the added subcellular localization and expression profile data to reveal the distinction between protein complexes and functional modules and found protein complexes may be the elements to dynamically form the functional modules. Unfortunately, at present, only a few clustering methods distinguish the protein complexes and functional modules.…”

Section: Introductionmentioning

confidence: 98%

“…Tang et al [15] constructed the dynamic protein interaction networks and illustrated the dynamic protein interaction network includs more protein information than the static network. Lu et al [16] presented a hierarchical clustering algorithm to extract the systemic and dynamic organization of biological networks by integrating high-throughput proteinprotein interaction data with the added subcellular localization and expression profile data. Komurov et al [17] identified dynamic modules and static modules on static network by means of gene expression profiles.…”

Section: Introductionmentioning

confidence: 99%

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Identification of protein complexes and functional modules in integrated PPI networks

Guo

Shang

Zhu

et al. 2014

2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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Mining the protein complexes and functional modules from protein-protein interaction (PPI) networks is vital to understand the mechanism of cellular components and protein functions. Most of the proposed methods had solely focused on static properties of the PPI networks since the available PPI data are static. However, cellular systems are highly dynamic. That is, the interactions of proteins are responsive to environmental cues to accomplish diverse cellular functions. It is important to consider the dynamic inherent within the PPI networks to identify protein complexes and functional modules. In addition, most computational methods did not distinguish between protein complexes and functional modules. It is important to distinguish between them since they are different protein organizations. In this paper, we propose a novel framework to analyze the PPI networks in dynamic conditions by integrating time-series gene expression profiles data and subcelluar localization data. The algorithm, CBMI, is developed to identify protein complexes in integrated PPI networks. By investigating multiple perspectives of proteins in the PPI networks, we identify the "dynamic" hubs in the PPI networks, and then present a new method to discover the functional modules in the PPI networks. The experimental results show that the integration of temporal gene expression data and subcelluar localization data with PPI data contributes to extracting the protein complexes more precisely.Comprehensive evaluations based on f-measure and functional annotations in MIPS database reveal that our algorithm, CBMI, outperforms other previous algorithms in identifying protein complexes, and the detected functional modules are statistically significant in terms of functional annotations. The proposed framework provides a new clue to distinguish between protein complexes and functional modules, and the developed algorithms can be an effective technique for the identification of them.

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Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Identification of protein complexes and functional modules in integrated PPI networks

Guo

Shang

Zhu

et al. 2014

2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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“…Specifically, when calculating the total score for a link between protein A and protein B, all of the confidence scores of the link between A and B in each subset are provided as input to an integrating function. For example, the Bayesian method has been widely used in the integration of different data sources, and has been shown as an effective approach [3][4][5][6]. Several integrative databases of protein-protein interactions have been generated with this integration approach(e.g.…”

Section: Introductionmentioning

confidence: 99%

PTOMSM: A modified version of Topological Overlap Measure used for predicting Protein-Protein Interaction Network

Huang

2009

Nature Precedings

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A variety of methods are developed to integrating diverse biological data to predict novel interaction relationship between proteins. However, traditional integration can only generate protein interaction pairs within existing relationships. Therefore, we propose a modified version of Topological Overlap Measure to identify not only extant direct PPIs links, but also novel protein interactions that can be indirectly inferred from various relationships between proteins. Our method is more powerful than a naïve Bayesian-network-based integration in PPI prediction, and could generate more reliable candidate PPIs. Furthermore, we examined the influence of the sizes of training and test datasets on prediction, and further demonstrated the effectiveness of PTOMSM in predicting PPI. More importantly, this method can be extended naturally to predict other types of biological networks, and may be combined with Bayesian method to further improve the prediction.

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“…[3][4][5] However, while the majority of the studies in this area use these networks in an isolated manner, a more comprehensive understanding of the cell requires an integration of different types of cellular interactions -one of the goals of systems biology. Although developments have been made in this direction, [6][7][8][9][10][11] most studies are limited to understanding particular sub-systems.…”

Section: Introductionmentioning

confidence: 99%

Extensive cross-talk and global regulators identified from an analysis of the integrated transcriptional and signaling network in Escherichia coli

2012

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To understand the regulatory dynamics of transcription factors (TFs) and their interplay with other cellular components we have integrated transcriptional, protein-protein and the allosteric or equivalent interactions which mediate the physiological activity of TFs in Escherichia coli. To study this integrated network we computed a set of network measurements followed by principal component analysis (PCA), investigated the correlations between network structure and dynamics, and carried out a procedure for motif detection. In particular, we show that outliers identified in the integrated network based on their network properties correspond to previously characterized global transcriptional regulators. Furthermore, outliers are highly and widely expressed across conditions, thus supporting their global nature in controlling many genes in the cell. Motifs revealed that TFs not only interact physically with each other but also obtain feedback from signals delivered by signaling proteins supporting the extensive cross-talk between different types of networks. Our analysis can lead to the development of a general framework for detecting and understanding global regulatory factors in regulatory networks and reinforces the importance of integrating multiple types of interactions in underpinning the interrelationships between them.

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Integrated analysis of multiple data sources reveals modular structure of biological networks

Cited by 34 publications

References 26 publications

Identification of protein complexes and functional modules in integrated PPI networks

Identification of protein complexes and functional modules in integrated PPI networks

PTOMSM: A modified version of Topological Overlap Measure used for predicting Protein-Protein Interaction Network

Extensive cross-talk and global regulators identified from an analysis of the integrated transcriptional and signaling network in Escherichia coli

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