Coherent network partitions

Angeleska, Aleksandra; Nikoloski, Zoran

doi:10.1016/j.dam.2019.02.048

Cited by 7 publications

(8 citation statements)

References 18 publications

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“…A coherent network partition in a graph

is a partition

of the node set

such that every

1

, induces disconnected subgraph in

, i.e. a biclique spanned subgraph in

( Angeleska and Nikoloski, 2019 ). A network partition

is obtained by removing all edges

1

, resulting in the clusters

.…”

Section: Resultsmentioning

confidence: 99%

“…Given a graph

that represents a PPI network, we hypothesize that an optimal coherent network partition corresponds to partitioning of the network into protein complexes. It has recently been shown that the problem of finding an optimal coherent network partition is NP-hard ( Angeleska and Nikoloski, 2019 ). Thus, in the following, we present a greedy approximation algorithm for solving this optimization problem, in which iteratively identify the node of the best score, defined below, together with the biclique spanned subgraph in which it participates followed by its removal from the network.…”

Section: Resultsmentioning

confidence: 99%

“…Further, such a formalization facilitated casting of the problem of protein complex prediction as that of finding an optimal coherent partition. Since this problem is NP-hard ( Angeleska and Nikoloski, 2019 ), we proposed a greedy approximation algorithm called PC2P, for protein complexes from coherent partition. In contrast to the existing algorithms, PC2P is parameter-free and, therefore, can be employed objectively, without user-specified and case-to-case tuned parameters.…”

Section: Discussionmentioning

confidence: 99%

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PC2P: parameter-free network-based prediction of protein complexes

2021

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Motivation Prediction of protein complexes from protein–protein interaction (PPI) networks is an important problem in systems biology, as they control different cellular functions. The existing solutions employ algorithms for network community detection that identify dense subgraphs in PPI networks. However, gold standards in yeast and human indicate that protein complexes can also induce sparse subgraphs, introducing further challenges in protein complex prediction. Results To address this issue, we formalize protein complexes as biclique spanned subgraphs, which include both sparse and dense subgraphs. We then cast the problem of protein complex prediction as a network partitioning into biclique spanned subgraphs with removal of minimum number of edges, called coherent partition. Since finding a coherent partition is a computationally intractable problem, we devise a parameter-free greedy approximation algorithm, termed Protein Complexes from Coherent Partition (PC2P), based on key properties of biclique spanned subgraphs. Through comparison with nine contenders, we demonstrate that PC2P: (i) successfully identifies modular structure in networks, as a prerequisite for protein complex prediction, (ii) outperforms the existing solutions with respect to a composite score of five performance measures on 75% and 100% of the analyzed PPI networks and gold standards in yeast and human, respectively, and (iii,iv) does not compromise GO semantic similarity and enrichment score of the predicted protein complexes. Therefore, our study demonstrates that clustering of networks in terms of biclique spanned subgraphs is a promising framework for detection of complexes in PPI networks. Availability and implementation https://github.com/SaraOmranian/PC2P. Supplementary information Supplementary data are available at Bioinformatics online.

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“…A coherent network partition in a graph

is a partition

of the node set

such that every

1

, induces disconnected subgraph in

, i.e. a biclique spanned subgraph in

( Angeleska and Nikoloski, 2019 ). A network partition

is obtained by removing all edges

1

, resulting in the clusters

.…”

Section: Resultsmentioning

confidence: 99%

“…Given a graph

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

PC2P: parameter-free network-based prediction of protein complexes

2021

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“…Here we propose a class of efficient greedy algorithms, collectively termed GCC-v, to predict protein complexes based on the concept of (weighted) clustering coefficient of nodes in a given PPI network. Our findings demonstrate that GCC-v partitions the network into biclique spanned subgraphs [40] , thus allowing the prediction of both sparse and dense protein complexes. The reason why GCC-v partitions the network into biclique spanned subgraphs is that for each of these subgraphs, say

, there exists a node whose first neighborhood contains

hence,

is necessarily spanned by a star, which is a biclique.…”

Section: Introductionmentioning

confidence: 83%

Efficient and accurate identification of protein complexes from protein-protein interaction networks based on the clustering coefficient

Omranian

Angeleska

Nikoloski

2021

Computational and Structural Biotechnology Journal

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“…In contrast to the existing computational approaches, PC2P, GCC-v, and CUBCO Omranian & Nikoloski, 2022) represent parameter-free algorithms and compare the performance of their results with several state-of-the-art approaches across different species. These approaches detect a protein complex based on partitioning the network into biclique spanned subgraphs, which is also known as coherent network partition (CNP) (Angeleska & Nikoloski, 2019;Angeleska, et al, 2021). PC2P and GCC-v rely on local properties of the network by nding the minimum cut in complement of the second neighborhood of a node and computing the clustering coe cient for each node to partition the network into biclique spanned subgraphs , respectively.…”

Section: Introductionmentioning

confidence: 99%

CUBCO+: Prediction of Protein Complexes Based on Min-cut Network Partitioning Into Biclique Spanned Subgraphs

Omranian

Nikoloski

2022

Preprint

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High-throughput proteomics approaches have resulted in large-scale protein-protein interaction (PPI) networks that have been employed for the prediction of protein complexes. However, PPI networks contain false-positive as well as false-negative PPIs that affect the protein complex prediction algorithms. To address this issue, here we propose an algorithm called CUBCO+ that: (1) employs GO semantic similarity to retain only biologically relevant interactions with high similarity score, (2) based on link prediction approaches, scores the false-negative edges, and (3) incorporates the resulting scores to predict protein complexes. Through comprehensive analyses with PPIs from Escherichia coli, Saccharomyces cerevisiae, and Homo sapiens, we show that CUBCO+ performs as well as the approaches that predict protein complexes based on recently introduced graph partitions into biclique spanned subgraphs and outperforms the other state-of-the-art approaches. Moreover, we illustrate that in combination with GO semantic similarity, CUBCO+ enables us to predict more accurate protein complexes in ~36% of the cases in comparison to CUBCO as its predecessor.

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Coherent network partitions

Cited by 7 publications

References 18 publications

PC2P: parameter-free network-based prediction of protein complexes

PC2P: parameter-free network-based prediction of protein complexes

Efficient and accurate identification of protein complexes from protein-protein interaction networks based on the clustering coefficient

CUBCO+: Prediction of Protein Complexes Based on Min-cut Network Partitioning Into Biclique Spanned Subgraphs

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