DPCT: A Dynamic Method for Detecting Protein Complexes From TAP-Aware Weighted PPI Network

SabziNezhad, Ali; Jalili, Saeed

doi:10.3389/fgene.2020.00567

Cited by 13 publications

(3 citation statements)

References 41 publications

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“…To inspect the performance of our proposed algorithm, we compare our algorithm with MCODE [6], Cfinder [4], ClusterOne [20], ProRank+ [27], MCL [28], PC2P [25], CLE [7], CW [8], CLP [29], CI [13], DPCT [30] in different measures as shown in Additional file 1: Table S1 and all the weighted graphs are constructed based on Eq. (5).…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

Protein complexes detection based on node local properties and gene expression in PPI weighted networks

Kong

2022

BMC Bioinformatics

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Background Identifying protein complexes from protein–protein interaction (PPI) networks is a crucial task, and many related algorithms have been developed. Most algorithms usually employ direct neighbors of nodes and ignore resource allocation and second-order neighbors. The effective use of such information is crucial to protein complex detection. Result Based on this observation, we propose a new way by combining node resource allocation and gene expression information to weight protein network (NRAGE-WPN), in which protein complexes are detected based on core-attachment and second-order neighbors. Conclusions Through comparison with eleven methods in Yeast and Human PPI network, the experimental results demonstrate that this algorithm not only performs better than other methods on 75% in terms of f-measure+, but also can achieve an ideal overall performance in terms of a composite score consisting of five performance measures. This identification method is simple and can accurately identify more complexes.

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Section: Comparison With Other Methodsmentioning

confidence: 99%

Protein complexes detection based on node local properties and gene expression in PPI weighted networks

Kong

2022

BMC Bioinformatics

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“…The gene modules were then imported into the STRING database (https://string-db.org/) to construct the protein-protein interaction (PPI) networks at different time points [29]. The minimum required interaction score was "medium con dence level" (0.400).…”

Section: Construction Of Protein-protein Interaction Networkmentioning

confidence: 99%

Temporal and spatial transcriptomic analysis of neutrophils and monocyte/macrophages in intracerebral hemorrhage

Pan,

Lan,

et al. 2023

Preprint

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Background and aim: Intracerebral hemorrhage (ICH) is a devastating stroke, in which inflammation plays an important role. We aim to understand the functions of neutrophils and monocyte/macrophage in inflammation of ICH. Materials and Methods: We comprehensively analyzed a publically-available transcriptomic dataset (GSE163256) at spatial and temporal levels. A gene co-expression network algorithm combined with Kpca algorithm was used to pinpoint the effective gene modules when setting the National Institute of Health Stroke Scale (NIHSS), modified Rankin Scale (mRS), and Glasgow Coma Scale (GCS) as outcome, and recognize the hub genes that influence the ICH prognosis when setting mRS as outcome. The logistic regression analysis was then conducted to identify the key genes. Results: In both peripheral blood (PB) and hematoma, cytokine-mediated signaling in neutrophils was associated with GCS; cytokine-mediated signaling processes and innate immune response processes in neutrophils were associated with NIHSS and mRS, respectively; energy metabolism in monocyte/macrophages was associated with NIHSS. In hematoma, leukocyte activation and migration in monocyte/macrophages correlated with GCS, while inflammation-related pathways correlated with mRS. Moreover, neutrophils experienced cytokine modulation and leukocyte-mediated immune responses, monocyte/macrophages underwent NF-κB-mediated immune responses in early ICH. The key genes included NCF1, GPSM3, STEAP4, ABDH16A, and PTPRC in neutrophils and NHP2, UPP1, CYBB, IDH1, and ZC3H12A in monocyte/macrophages. Conclusion: Our findings suggest a mechanism involving inflammation in ICH, in which neutrophils are activated in PB and hematoma, which in turn recruit monocytes into hematoma where they differentiate to macrophages and are activated to promote inflammation, contributing to secondary brain injury.

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“…However, potential data redundancy issues can arise within Protein-Protein Interaction (PPI) networks. To address this challenge, the DPCT algorithm [6] integrates TAP and GO data to construct a weighted network, employs a memetic algorithm for clustering individual subnetworks, and subsequently eliminates overlapping clusters to achieve dynamic detection. While these methods can partially capture dynamics by analyzing protein activity, they do not fully address the computational aspects of simulating dynamic networks.…”

Section: Introductionmentioning

confidence: 99%

Network clustering algorithm for dynamic protein complex detection fused graph embedding

Wang,

Jia,

Yang

2024

Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023)

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Network clustering for protein complex identification within protein interaction networks has long been a focal point for researchers in the field of machine learning and data mining. Although existing algorithms take into account the dynamic properties of protein complexes, they ignore the computational dynamics. This paper introduces a novel network clustering algorithm designed for dynamic protein complex detection. This approach integrates gene expression data and static network data to construct a dynamic network construction model and incorporates multiple data sources to formulate a node representation model. It devises a new dynamic clustering model that simulates the splitting, merging, and growth of protein complexes, providing a framework for dynamic network clustering. Experimental results demonstrate that the proposed algorithm outperforms dynamic clustering algorithms and traditional clustering algorithms.

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DPCT: A Dynamic Method for Detecting Protein Complexes From TAP-Aware Weighted PPI Network

Cited by 13 publications

References 41 publications

Protein complexes detection based on node local properties and gene expression in PPI weighted networks

Protein complexes detection based on node local properties and gene expression in PPI weighted networks

Temporal and spatial transcriptomic analysis of neutrophils and monocyte/macrophages in intracerebral hemorrhage

Network clustering algorithm for dynamic protein complex detection fused graph embedding

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