DPPIN: A Biological Repository of Dynamic Protein-Protein Interaction Network Data

Fu, Dongqi; He, Jingrui

doi:10.1109/bigdata55660.2022.10020904

Cited by 6 publications

(3 citation statements)

References 56 publications

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“…G V E , with proteins as nodes and interactions between proteins as edges, where V represents the set of nodes, and E represents the set of edges [11][12] . A dynamic PPI network can be viewed as a static subnetwork within the time series, effectively without direction.…”

Section: Preliminarymentioning

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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Section: Preliminarymentioning

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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“…Gehlenborg et al surveyed the applications in systems biology [GOB * 10] and concluded that node-link diagrams dominate visualizations in this field because of their ability to easily perform path-related tasks while encoding dynamic relationships. Common examples of applications in this space include biological networks [SDMW09], evolution of metabolic pathways [RUK * 10], protein interactions [WPPW14,FH21], and chemical reaction networks [GV20]. Complex attributes on both nodes and edges is a common characteristic of these networks, which are typically encoded using either juxtaposed or nested marks.…”

Section: Physical and Life Sciencesmentioning

confidence: 99%

The State of the Art in Visualizing Dynamic Multivariate Networks

Kale

Sun

Papka

2023

Computer Graphics Forum

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Most real‐world networks are both dynamic and multivariate in nature, meaning that the network is associated with various attributes and both the network structure and attributes evolve over time. Visualizing dynamic multivariate networks is of great significance to the visualization community because of their wide applications across multiple domains. However, it remains challenging because the techniques should focus on representing the network structure, attributes and their evolution concurrently. Many real‐world network analysis tasks require the concurrent usage of the three aspects of the dynamic multivariate networks. In this paper, we analyze current techniques and present a taxonomy to classify the existing visualization techniques based on three aspects: temporal encoding, topology encoding, and attribute encoding. Finally, we survey application areas and evaluation methods; and discuss challenges for future research.

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“…Infectious SocioPatterns dynamic contact networks [35]: This dataset contains the daily dynamic contacts (edges) between the persons (nodes) collected during the Infectious SocioPatterns event that took place at the Science Gallery in Dublin, Ireland. Dynamic protein-protein interaction (DPPI) network [36]: here, a DPPI network is constructed using activity and co-expression analysis. Specifically, this dataset stores temporal connections of the generated dynamic network, where a node represents a gene coding protein retrieved from Saccharomyces Genome Database, and an edge represents a protein-protein interaction in terms of protein functional associations at a certain timestamp.…”

Section: Performance Of Otahamentioning

confidence: 99%

Detecting the driver nodes of temporal networks

Qin

Duan

2023

New J. Phys.

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Detecting the driver nodes of complex networks has garnered significant attention recently to control complex systems to desired behaviors, where nodes represent system components and edges encode their interactions. Driver nodes, which are directly controlled by external inputs, play a crucial role in controlling all network nodes. While many approaches have been proposed to identify driver nodes of static networks, we still lack an effective algorithm to control ubiquitous temporal networks, where network structures evolve over time. Here we propose an effective online time-accelerated heuristic algorithm (OTaHa) to detect driver nodes of temporal networks. Together with theoretical analysis and numerical simulations on synthetic and empirical temporal networks, we show that OTaHa offers multiple sets of driver nodes, and noticeably outperforms existing methods in terms of accuracy and execution time. We further report that most edges are redundant in controlling temporal networks although the complete instantaneous signal-carrying edges cannot be guaranteed. Moreover, removing edges with high edge betweenness (the number of all-pairs shortest paths passing through the edge) significantly impedes the overall controllability. Our work provides an effective algorithm and paves the way for subsequent explorations on achieving the ultimate control of temporal networks.

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DPPIN: A Biological Repository of Dynamic Protein-Protein Interaction Network Data

Cited by 6 publications

References 56 publications

Network clustering algorithm for dynamic protein complex detection fused graph embedding

Network clustering algorithm for dynamic protein complex detection fused graph embedding

The State of the Art in Visualizing Dynamic Multivariate Networks

Detecting the driver nodes of temporal networks

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