Detecting Functional Modules Based on a Multiple-Grain Model in Large-Scale Protein-Protein Interaction Networks

Ji, Junzhong; Yang, Cuicui; Zhang, Aidong

doi:10.1109/tcbb.2015.2480066

Cited by 5 publications

(4 citation statements)

References 44 publications

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“…Existing studies demonstrate that the genes or gene products with similar expression patterns tend to have the similar biological function in a period of life, and also more likely to contact each other to form a dense functional module in PPI networks [16]. Therefore, proteins' GED data are used in this work to evaluate the similarity of proteins in a PPI network [8,17]. Ji et al [17] introduced a multiplegrain model to detect functional modules from large-scale PPI networks.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, proteins' GED data are used in this work to evaluate the similarity of proteins in a PPI network [8,17]. Ji et al [17] introduced a multiplegrain model to detect functional modules from large-scale PPI networks. Spirin et al [8] presented an enumeration method to find completely connected subgraphs, and then to search for protein functional module.…”

Section: Related Workmentioning

confidence: 99%

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Parallel Protein Community Detection in Large-scale PPI Networks Based on Multi-source Learning

Chen

Bilal

et al. 2024

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Protein interactions constitute the fundamental building block of almost every life activity. Identifying protein communities from Protein-Protein Interaction (PPI) networks is essential to understand the principles of cellular organization and explore the causes of various diseases. It is critical to integrate multiple data resources to identify reliable protein communities that have biological significance and improve the performance of community detection methods for large-scale PPI networks. In this paper, we propose a Multi-source Learning based Protein Community Detection (MLPCD) algorithm by integrating Gene Expression Data (GED) and a parallel solution of MLPCD using cloud computing technology. GED under different conditions is integrated with the original PPI network to reconstruct a Weighted-PPI network. To flexibly identify protein communities of different scales, we define community modularity and functional cohesion measurements and detect protein communities from WPPI. In addition, we compare the detected communities with known protein complexes and evaluate the function enrichment of protein functional modules using Gene Ontology annotations. We implement a parallel version of MLPCD on the Apache Spark platform to enhance the performance of the algorithm. Extensive experimental results indicate the superiority and notable advantages of the MLPCD algorithm over the relevant algorithms in terms of accuracy and performance.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Parallel Protein Community Detection in Large-scale PPI Networks Based on Multi-source Learning

Chen

Bilal

et al. 2024

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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“…PPI network is subdivided into various modules based on functionality or biological similarity [13]. If the topological and functional similarities are present, then they can be merged under a particular functional module [14].…”

Section: Biological Assessmentmentioning

confidence: 99%

Alignment of Protein Interaction Networks and Disease Prediction: A Survey

Ali¹

2019

IJATCSE

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Proteins interact each other to perform many cellular activities. These interactions can be considered as Protein Protein Interaction networks (PPI). Interacting proteins form protein complexes. Mapping nodes between networks is denoted as alignment. The main intention of network alignment approach is to identify the protein complexes, which in turn helps to identify the functionality of protein complexes in various cellular systems. These interactome units form the conserved pathways between the networks. So network alignment requires lot of attention and several algorithms and techniques have been proposed to address this. The study of PPI is widely recognized to know more about the underlying complex disease because proteins associated with any disease get connected and form subgraphs or pathways. In this paper, the authors compared the various aligners, the performance evaluation metrics, the common databases used for PPI evaluation and the importance of PPI network in biomedical research.

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“…The threshold value of similarity defines the minimum amount of similarity required for alignment. PPI network can be subdivided into modules based on functionality or biological similarity [13]. Many researchers have proposed methods to scale large PPI network into smaller graphs.…”

Section: B Semantic Similarity Assessmentmentioning

confidence: 99%

A review of aligners for protein protein interaction networks

Ali

Viswanath

Patil

et al. 2017

2017 2nd IEEE International Conference on Recent Trends in Electronics, Information &Amp; Communication Technology (RTEICT)

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Protein Protein Interaction (PPI) can be considered as network. Alignment is the process of mapping nodes from one network to another network. The main objective of network alignment is to identify small, well defined interactome units such as protein complexes or conserved pathways that are analogous in the input network. Network alignment uncovers the relationship between protein complexes and functions. Similarity between two graph structures can be identified by evaluating the topology. Network alignment identifies either topological or sequential similarity. Gene annotations reveal the functional or sequential similarity and it can be evaluated based on semantic similarity. In this paper, we review the various network aligners and classify them according to the methodologies. We discuss the different evaluation metrics and the popular databases of protein interactions.

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Detecting Functional Modules Based on a Multiple-Grain Model in Large-Scale Protein-Protein Interaction Networks

Cited by 5 publications

References 44 publications

Parallel Protein Community Detection in Large-scale PPI Networks Based on Multi-source Learning

Parallel Protein Community Detection in Large-scale PPI Networks Based on Multi-source Learning

Alignment of Protein Interaction Networks and Disease Prediction: A Survey

A review of aligners for protein protein interaction networks

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