Core and peripheral connectivity based cluster analysis over PPI network

Ahmed, Hasin Afzal; Bhattacharyya, Dhruba K.; Kalita, Jugal

doi:10.1016/j.compbiolchem.2015.08.008

Cited by 15 publications

(12 citation statements)

References 46 publications

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“…In order to evaluate the effectiveness of ICJointLE, we compared ICJointLE with ten other existing methods PCP*, MCL [16], WPNCA [27], APcluster [15], SPICi [20], ClusterONE [19], WEC [36], RNSC [29], CORE [22], and MCODE [17]. PCP* is the extension of PCP [12], where the threshold FS is added to filter out the interactions with reliability score lower than FS.…”

Section: Identification Resultsmentioning

confidence: 99%

“…The p-value of an identified complex has close association with the size of the identified complex [22]. In order to further compare the proportion of the identified significant complexes of different size, we partitioned the identified complexes into three groups.…”

Section: Data Sets Methodsmentioning

confidence: 99%

“…The maximum matching ratio (MMR) [19] is based on a maximal one-to-one mapping between identified complex and known complex. And MMR is calculated by formula (22).…”

Section: Statistical Matching Based Metricsmentioning

confidence: 99%

“…Hasin et al [22] pointed out that a protein complex typically has two regions, viz., core and periphery. The core part is a highly dense central region where proteins are strongly connected with each other, and periphery region is a part of the complex where proteins are weakly connected with the core [22]. In fact, the concept of the core/periphery structure is originated from the core-attachment structure.…”

Section: Introductionmentioning

confidence: 99%

“…In fact, the concept of the core/periphery structure is originated from the core-attachment structure. In this paper, the core/periphery structure is synonymous with the core-attachment structure [22]. In [23][24][25][26][27], the idea of the core-attachment structure is exploited to detect protein complexes.…”

Section: Introductionmentioning

confidence: 99%

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A method for identifying protein complexes with the features of joint co-localization and joint co-expression in static PPI networks

Zhang

Zhong

Huang

et al. 2019

Computers in Biology and Medicine

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Identifying protein complexes in static protein-protein interaction (PPI) networks is essential for understanding the underlying mechanism of biological processes. Proteins in a complex are co-localized at the same place and co-expressed at the same time. We propose a novel method to identify protein complexes with the features of joint co-localization and joint co-expression in static PPI networks. To achieve this goal, we define a joint localization vector to construct a joint co-localization criterion of a protein group, and define a joint gene expression to construct a joint co-expression criterion of a gene group. Moreover, the functional similarity of proteins in a complex is an important characteristic. Thus, we use the CC-based, MF-based, and BP-based protein similarities to devise functional similarity criterion to determine whether a protein is functionally similar to a protein cluster. Based on the core-attachment structure and following to seed expanding strategy, we use four types of biological data including PPI data with reliability score, protein localization data, gene expression data, and gene ontology annotations, to identify protein complexes. The experimental results on yeast data show that comparing with existing methods our proposed method can efficiently and exactly identify more protein complexes, especially more protein complexes of sizes from 2 to 6. Furthermore, the enrichment analysis demonstrates that the protein complexes identified by our method have significant biological meaning.

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Section: Identification Resultsmentioning

confidence: 99%

Section: Data Sets Methodsmentioning

confidence: 99%

“…The maximum matching ratio (MMR) [19] is based on a maximal one-to-one mapping between identified complex and known complex. And MMR is calculated by formula (22).…”

Section: Statistical Matching Based Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%