A core-attachment based method to detect protein complexes in PPI networks

Wu, Min; Li, Xiaoli; Kwoh, Chee Keong; Ng, See-Kiong

doi:10.1186/1471-2105-10-169

Cited by 338 publications

(341 citation statements)

References 54 publications

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“…This is because practically all methods find it difficult to detect small complexes (consisting of fewer than four proteins) and hence explicitly exclude these complexes from their predictions (e.g. see [55]). Besides, the possibility of a predicted complex matching a reference complex that is small purely by chance is relatively high [61], and therefore evaluating the methods becomes challenging (further discussed in Section 2.4.3).…”

Section: Comparative Assessment Of Complex Detection Methodsmentioning

confidence: 99%

“…CORE [54], COACH [55], MCL-CAw [56,57] and CACHET [58] look for clusters that adhere to the core-attachment organization, noted originally in yeast complexes by Gavin et al [6]. Large-scale pull-down of yeast complexes using TAP-MS in [6] revealed that proteins within complexes are organized as two distinct sets: cores that constitute central functional units of complexes, and attachments that aid core proteins in their functions.…”

Section: Methods Incorporating Core-attachment Structurementioning

confidence: 99%

“…COACH [55] works by identifying small dense neighbourhoods around proteins with high degrees in the PPI network. These dense subnetworks are then merged to generate cores.…”

Section: Methods Incorporating Core-attachment Structurementioning

confidence: 99%

“…As a result, most methods fare poorly in detecting small complexes (evaluated in [26,75]) or explicitly exclude small complexes from their predictions (e.g. see [55]). Detection of small complexes therefore requires specialized methods.…”

Section: Detection Of Small Complexesmentioning

confidence: 99%

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Methods for protein complex prediction and their contributions towards understanding the organisation, function and dynamics of complexes

et al. 2015

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Complexes of physically interacting proteins constitute fundamental functional units responsible for driving biological processes within cells. A faithful reconstruction of the entire set of complexes is therefore essential to understand the functional organization of cells. In this review, we discuss the key contributions of computational methods developed till date (approximately between 2003 and 2015) for identifying complexes from the network of interacting proteins (PPI network). We evaluate in depth the performance of these methods on PPI datasets from yeast, and highlight challenges faced by these methods, in particular detection of sparse and small or sub-complexes and discerning of overlapping complexes. We describe methods for integrating diverse information including expression profiles and 3D structures of proteins with PPI networks to understand the dynamics of complex formation, for instance, of time-based assembly of complex subunits and formation of fuzzy complexes from intrinsically disordered proteins. Finally, we discuss methods for identifying dysfunctional complexes in human diseases, an application that is proving invaluable to understand disease mechanisms and to discover novel therapeutic targets. We hope this review aptly commemorates a decade of research on computational prediction of complexes and constitutes a valuable reference for further advancements in this exciting area.

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Section: Comparative Assessment Of Complex Detection Methodsmentioning

confidence: 99%

Section: Methods Incorporating Core-attachment Structurementioning

confidence: 99%

“…COACH [55] works by identifying small dense neighbourhoods around proteins with high degrees in the PPI network. These dense subnetworks are then merged to generate cores.…”

Section: Methods Incorporating Core-attachment Structurementioning

confidence: 99%

Section: Detection Of Small Complexesmentioning

confidence: 99%

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Methods for protein complex prediction and their contributions towards understanding the organisation, function and dynamics of complexes

et al. 2015

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“…Gavin et al proposed that a protein complex consists of high-density core proteins and some attachment proteins. Several complex detection algorithms have been developed based on this notion [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Partitioning a PPI Network into Overlapping Modules Constrained by High-Density and Periphery Tracking

Altaf-Ul-Amin

Wada

Kanaya

2012

ISRN Biomathematics

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This paper presents an algorithm called DPClusO for partitioning simple graphs into overlapping modules, that is, clusters constrained by density and periphery tracking. The major advantages of DPClusO over the related and previously published algorithm DPClus are shorter running time and ensuring coverage, that is, each node goes to at least one module. DPClusO is a general-purpose clustering algorithm and useful for finding overlapping cohesive groups in a simple graph for any type of application. This work shows that the modules generated by DPClusO from several PPI networks of yeast with high-density constraint match with more known complexes compared to some other recently published complex generating algorithms. Furthermore, the biological significance of the high density modules has been demonstrated by comparing their P values in the context of Gene Ontology (GO) terms with those of the randomly generated modules having the same size, distribution, and zero density. As a consequence, it was also learnt that a PPI network is a combination of mainly high-density and star-like modules.

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Community detection based on social interactions in a social network

Chen

Chuang

Chiu

2014

Asso for Info Science & Tech

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Recent research has involved identifying communities in networks. Traditional methods of community detection usually assume that the network's structural information is fully known, which is not the case in many practical networks. Moreover, most previous community detection algorithms do not differentiate multiple relationships between objects or persons in the real world. In this article, we propose a new approach that utilizes social interaction data (e.g., users' posts on Facebook) to address the community detection problem in Facebook and to find the multiple social groups of a Facebook user. Some advantages to our approach are (a) it does not depend on structural information, (b) it differentiates the various relationships that exist among friends, and (c) it can discover a target user's multiple communities. In the experiment, we detect the community distribution of Facebook users using the proposed method. The experiment shows that our method can achieve the result of having the average scores of Total-Community-Purity and Total-Cluster-Purity both at approximately 0.8.

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A core-attachment based method to detect protein complexes in PPI networks

Cited by 338 publications

References 54 publications

Methods for protein complex prediction and their contributions towards understanding the organisation, function and dynamics of complexes

Methods for protein complex prediction and their contributions towards understanding the organisation, function and dynamics of complexes

Partitioning a PPI Network into Overlapping Modules Constrained by High-Density and Periphery Tracking

Community detection based on social interactions in a social network

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