Local-community network automata modelling based on length-three-paths for prediction of complex network structures in protein interactomes, food webs and more

Muscoloni, Alessandro; Abdelhamid, Ilyes; Cannistraci, Carlo Vittorio

doi:10.1101/346916

Cited by 31 publications

(57 citation statements)

References 18 publications

(39 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…3d) the ranking of the methods is similar to the one seen in Football (Fig. 3a), with L3 methods as best performing (in agreement with link prediction results of Muscoloni et al 28 ), followed by CH-L2 methods (CH2-L2, CH3-L2 and RA-L2), and then by the other L2-based predictors (in agreement with the results of Cannistraci 32 , discussing the importance of minimizing external links in PPI networks). We confirm that also in our tests L3-methods overcome L2-methods as reported in previous literature by Kovács et al 29 .…”

Section: Hyperedge Entanglement Predictor (Hep)supporting

confidence: 83%

Hyperedge Entanglement in High-Order Multilayer Networks

Muscoloni¹,

Abdelhamid²,

Decano³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Many real complex systems present multilayer structure where high-order metadata on one layer refers to dyadic data on a lower layer. Significant progresses to analyse high-order metadata under the assumption of community organization have been done. However, there are no planted communities in real-world networks, and the necessity of new frameworks to analyze high-order metadata regardless of community organization has been raised.Here, we propose to adopt hyperedge organization. Predicting ‘entanglements’ between a hyperedge and nodes scattered in the rest of the network might suggest structural or functional liaisons, without assumption of any community organization. We introduce a novel concept: hyperedge entanglement (HE), which associates to each hyperedge an entangled hyperedge, by means of a network operator that predicts significant ‘interactions at distance’ between network nodes and existing hyperedges. We also introduce a new challenge termed hyperedge entanglement prediction (HEP), and an algorithm to perform this task. We evaluated HEP performance on social, biological and synthetic data where, given only topology and hyperedges (such as communities or functional modules), the goal is to predict whether nodes not connected to a certain hyperedge might be candidates for a significant entanglement. Finally, as real application in diseasome systems biomedicine, we perform HEP on the human protein interactome to predict unknown gene entanglements with the COPD disease gene hyperedge. HEP predictions are validated by biological experiments, enlarging our understanding of molecular mechanisms behind COPD/aneurysm comorbidity.

show abstract

Section: Hyperedge Entanglement Predictor (Hep)supporting

confidence: 83%

Hyperedge Entanglement in High-Order Multilayer Networks

Muscoloni¹,

Abdelhamid²,

Decano³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…These common characteristics have inspired scholars to study PPI networks in the way of studying social networks. These methodologies are mainly divided into three categories: neighborhood-based or paths-based approaches (Cannistraci et al, 2013;Huang et al, 2017;Muscoloni et al, 2018;Kovács et al, 2019;Pech et al, 2019), hierarchical clustering approaches (Clauset et al, 2008;Symeonidis et al, 2013), and random walkbased approaches (Lichtenwalter et al, 2010;Backstrom and Leskovec, 2011).…”

Section: Introductionmentioning

confidence: 99%

“…However, the principle that people tend to build relationships with people who are close to them in social networks cannot explain the interaction of two proteins. Therefore, some scholars (Muscoloni et al, 2018;Kovács et al, 2019;Pech et al, 2019) attempt to explain the link mechanism of PPI networks with 3-hop paths rather than 2-hop paths.…”

Section: Introductionmentioning

confidence: 99%

Protein Interface Complementarity and Gene Duplication Improve Link Prediction of Protein-Protein Interaction Network

et al. 2020

View full text Add to dashboard Cite

Protein-protein interactions are the foundations of cellular life activities. At present, the already known protein-protein interactions only account for a small part of the total. With the development of experimental and computing technology, more and more PPI data are mined, PPI networks are more and more dense. It is possible to predict protein-protein interaction from the perspective of network structure. Although there are many high-throughput experimental methods to detect protein-protein interactions, the cost of experiments is high, time-consuming, and there is a certain error rate meanwhile. Network-based approaches can provide candidates of protein pairs for high-throughput experiments and improve the accuracy rate. This paper presents a new link prediction approach "Sim" for PPI networks from the perspectives of proteins' complementary interfaces and gene duplication. By integrating our approach "Sim" with the state-of-art network-based approach "L3," the prediction accuracy and robustness are improved.

show abstract

“…Besides the above embedding techniques, we also compare our embedding model with 5 state-of-the-art network structure-based link prediction algorithms, including common neighbors (CN), preferential attachment (PA), Adamic Adar (AA), L 3 principle [17] as well as its higher-order extension (CH2-L3) [18].…”

Section: Baseline Modelmentioning

confidence: 99%

“…One class of simple yet efficient approaches for link prediction is called network structured-based algorithms, rooted in social network analysis. Network structure-based algorithms [13][14][15][16][17][18] such as common neighbors (CN), preferential attachment (PA), Adamic-Adar (AA) and the number of 3-hop paths (L 3 ), which assign a likelihood score to all candidate links (i.e., non-connected node pairs) and rank these unknown links according to their scores. Due to the simplicity and low computational complexity of network-based algorithms, they have obtained wide practical uses (e.g., Identification of novel protein interactions).…”

Section: Introductionmentioning

confidence: 99%

Graph embedding-based novel protein interaction prediction via higher-order graph convolutional network

Xiao,

Deng

2020

PLoS ONE

View full text Add to dashboard Cite

Protein-protein interactions (PPIs) are essential for most biological processes. However, current PPI networks present high levels of noise, sparseness and incompleteness, which limits our ability to understand the cell at the system level from the PPI network. Predicting novel (missing) links in noisy PPI networks is an essential computational method for automatically expanding the human interactome and for identifying biologically legitimate but undetected interactions for experimental determination of PPIs, which is both expensive and time-consuming. Recently, graph convolutional networks (GCN) have shown their effectiveness in modeling graph-structured data, which employ a 1-hop neighborhood aggregation procedure and have emerged as a powerful architecture for node or graph representations. In this paper, we propose a novel node (protein) embedding method by combining GCN and PageRank as the latter can significantly improve the GCN's aggregation scheme, which has difficulty in extending and exploring topological information of networks across higher-order neighborhoods of each node. Building on this novel node embedding model, we develop a higher-order GCN variational auto-encoder (HO-VGAE) architecture, which can learn a joint node representation of higher-order local and global PPI network topology for novel protein interaction prediction. It is worth noting that our method is based exclusively on network topology, with no protein attributes or extra biological features used. Extensive computational validations on PPI prediction task demonstrate our method without leveraging any additional biological information shows competitive performance-outperforms all existing graph embedding-based link prediction methods in both accuracy and robustness.

show abstract

Local-community network automata modelling based on length-three-paths for prediction of complex network structures in protein interactomes, food webs and more

Cited by 31 publications

References 18 publications

Hyperedge Entanglement in High-Order Multilayer Networks

Hyperedge Entanglement in High-Order Multilayer Networks

Protein Interface Complementarity and Gene Duplication Improve Link Prediction of Protein-Protein Interaction Network

Graph embedding-based novel protein interaction prediction via higher-order graph convolutional network

Contact Info

Product

Resources

About