Coherent network partitions: Characterizations with cographs and prime graphs

Angeleska, Aleksandra; Omranian, Sara; Nikoloski, Zoran

doi:10.1016/j.tcs.2021.10.002

Cited by 4 publications

(7 citation statements)

References 20 publications

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“…Thereby, the problem of protein complex prediction is cast to partition graph G, C = {C 1 , C 2 , …, C k }, such that each C i is a biclique spanned subgraph (Angeleska & Nikoloski, 2019) by removing a minimum number of edges. It has been shown that the problem of nding an optimal coherent network partition is NP-hard (Angeleska & Nikoloski, 2019), while a graph transformation has also been proposed to obtain a O(logn)approximation algorithm on a bipartite graph (Angeleska, et al, 2021). Thus, greedy approximation algorithms, including PC2P, GCC-v, and CUBCO, have been proposed for solving this optimization problem.…”

Section: Cubco + Algorithm Predicts Protein Complexes By Considering ...mentioning

confidence: 99%

“…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%

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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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Section: Cubco + Algorithm Predicts Protein Complexes By Considering ...mentioning

confidence: 99%

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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“…2022;37:6686-6728. wileyonlinelibrary.com/journal/int research topics in the era of big data. [1][2][3][4][5][6] Early research found that some graph-based network parameters (for instance, toughness) can directly measure the vulnerability of the network, and are directly related to the existence of the fractional factor and the -factor. This study models the network as a graph structure, and studies the relationship between graph parameters and the stability of the network structure and data transmission from a theoretical point of view.…”

Section: Introductionmentioning

confidence: 99%

“…As a common network representation framework, the graph topology is a crucial area in computer networks. The parameters defined on the network graph are the focus of network security, and graph dynamic flow modeling and network topology analysis have become hot research topics in the era of big data 1–6 . Early research found that some graph‐based network parameters (for instance, toughness) can directly measure the vulnerability of the network, and are directly related to the existence of the fractional factor and the

H

‐factor.…”

Section: Introductionmentioning

confidence: 99%

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Viewing the network parameters and H‐factors from the perspective of geometry

Gao

Chen

Zhang

2022

Int J of Intelligent Sys

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Recent studies have shown that there is a profound connection between the existence of -factors under attack circumstances and the parameters to measure the vulnerability of the network. The disadvantage of the previous theoretical conclusions is that the connectivity is often regarded as fixed, and the relationship between other network parameters and the -factor is explored. However, in a real situation, as the network structure changes, connectivity becomes a dynamically variational parameter, and its changing will affect other parameters to make corresponding changes. In | THE EXISTENCE OF ℋ-FACTOR FROM THE PERSPECTIVE OF HIGH DIMENSIONAL SPACE | Existing problems and motivationAlthough there are considerable amount of results about the toughness-related parameters of the -factor at present, we have found some problems from them, and the main problem is to focus on the relationship between toughness and connectivity.

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CUBCO+: prediction of protein complexes based on min-cut network partitioning into biclique spanned subgraphs

Omranian

Nikoloski

2022

Appl Netw Sci

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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 a 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.

show abstract

Coherent network partitions: Characterizations with cographs and prime graphs

Cited by 4 publications

References 20 publications

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

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

Viewing the network parameters and H‐factors from the perspective of geometry

CUBCO+: prediction of protein complexes based on min-cut network partitioning into biclique spanned subgraphs

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