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

Chen, Yu; Wang, Wei; Liu, Jiale; Feng, Jinping; Gong, Xinqi

doi:10.3389/fgene.2020.00291

Cited by 24 publications

(26 citation statements)

References 40 publications

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“…The average shortest path length of the dual-layered network (5.39), which can be interpreted as another representation of the diameter of a network, was also similar to the calculations of 43 organisms obtained by Jeong et al (2000). The duallayered network density was very low (0.001) and comparable with the density value of a calculated A. thaliana PPI network (Chen et al, 2020). Biological networks are expected to show sparse connection features, which means that the number of edges tends to be much smaller than that of the number of possible edges.…”

Section: The Modularity Of Sugarcane Dual-layered Networksupporting

confidence: 78%

“…Most of the nodes had small clustering coefficients in the PPI layer, which led to the calculated ACC equal to 0.10. Although the calculated ACC for the PPI layer alone was similar to what has already been shown in other organisms such as Saccharomyces cerevisiae (Uetz et al, 2000), the ACC of plants PPI networks established until now varies largely, from 0.06 in A. thaliana to 0.35 in Oryza (Chen et al, 2020). Nevertheless, it is known that PPI networks may not reflect the total interactome of an organism due to experimental limitations and that partial sampling can lower the clustering coefficient of networks (Friedel and Zimmer, 2006).…”

Section: The Modularity Of Sugarcane Dual-layered Networksupporting

confidence: 74%

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Arabidopsis-Based Dual-Layered Biological Network Analysis Elucidates Fully Modulated Pathways Related to Sugarcane Resistance on Biotrophic Pathogen Infection

Rody

Camargo

Creste³

et al. 2021

Front. Plant Sci.

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We assembled a dual-layered biological network to study the roles of resistance gene analogs (RGAs) in the resistance of sugarcane to infection by the biotrophic fungus causing smut disease. Based on sugarcane-Arabidopsis orthology, the modeling used metabolic and protein-protein interaction (PPI) data from Arabidopsis thaliana (from Kyoto Encyclopedia of Genes and Genomes (KEGG) and BioGRID databases) and plant resistance curated knowledge for Viridiplantae obtained through text mining of the UniProt/SwissProt database. With the network, we integrated functional annotations and transcriptome data from two sugarcane genotypes that differ significantly in resistance to smut and applied a series of analyses to compare the transcriptomes and understand both signal perception and transduction in plant resistance. We show that the smut-resistant sugarcane has a larger arsenal of RGAs encompassing transcriptionally modulated subnetworks with other resistance elements, reaching hub proteins of primary metabolism. This approach may benefit molecular breeders in search of markers associated with quantitative resistance to diseases in non-model systems.

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Section: The Modularity Of Sugarcane Dual-layered Networksupporting

confidence: 78%

Section: The Modularity Of Sugarcane Dual-layered Networksupporting

confidence: 74%

Arabidopsis-Based Dual-Layered Biological Network Analysis Elucidates Fully Modulated Pathways Related to Sugarcane Resistance on Biotrophic Pathogen Infection

Rody

Camargo

Creste³

et al. 2021

Front. Plant Sci.

View full text Add to dashboard Cite

show abstract

“…Due to the rapid development of experimental and computing technology, a large number of PPI networks have been mined ( Chen et al, 2020 ). Previous studies have reported that a PPI network can be constructed as a scale-free complex network and satisfies small-world property and high degree of clustering ( Ji et al, 2012 ).…”

Section: Introductionmentioning

confidence: 99%

Overlapping Structures Detection in Protein-Protein Interaction Networks Using Community Detection Algorithm Based on Neighbor Clustering Coefficient

et al. 2021

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With the rapid development of bioinformatics, researchers have applied community detection algorithms to detect functional modules in protein-protein interaction (PPI) networks that can predict the function of unknown proteins at the molecular level and further reveal the regularity of cell activity. Clusters in a PPI network may overlap where a protein is involved in multiple functional modules. To identify overlapping structures in protein functional modules, this paper proposes a novel overlapping community detection algorithm based on the neighboring local clustering coefficient (NLC). The contributions of the NLC algorithm are threefold: (i) Combine the edge-based community detection method with local expansion in seed selection and the local clustering coefficient of neighboring nodes to improve the accuracy of seed selection; (ii) A method of measuring the distance between edges is improved to make the result of community division more accurate; (iii) A community optimization strategy for the excessive overlapping nodes makes the overlapping structure more reasonable. The experimental results on standard networks, Lancichinetti-Fortunato-Radicchi (LFR) benchmark networks and PPI networks show that the NLC algorithm can improve the Extended modularity (EQ) value and Normalized Mutual Information (NMI) value of the community division, which verifies that the algorithm can not only detect reasonable communities but also identify overlapping structures in networks.

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“…Here, we applied six different types of link prediction algorithm. They are, Adamic and Adar Coefficient (AA) [50,52], Common Neighbor (CN) [28,50,53], Jaccard Coefficient (JAC) [28,50,54], Resource Allocation (RA) [50,55,56], Multiple Paths of Length L=3 (L3) [45,57], and Dice Coefficient (Dice) [58,59]. All of these algorithms have their scoring function.…”

Section: Neighbourhood-based Similarity-based Link Predictionmentioning

confidence: 99%

FDMine: a graph mining approach to predict and evaluate food-drug interactions

Rahman¹,

Vadrev²,

Magana-Mora

et al. 2021

Preprint

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Food-drug interactions (FDIs) arise when nutritional dietary consumption regulates biochemical mechanisms involved in drug metabolism. These interactions can create unexpected adverse pharmacological effects. By contrast, particular foods can aid in the recovery process of a patient. Towards characterizing the nature of food’s influence on pharmacological treatment, it is essential to detect all possible FDIs. In this study, we propose FDMine, a novel systematic framework that models the FDI problem as a homogenous graph. In this graph, all nodes representing drug, food and food composition are referenced as chemical structures. This homogenous representation enables us to take advantage of reported drug-drug interactions for accuracy evaluation, especially when accessible ground truth for FDIs is lacking. Our dataset consists of 788 unique approved small molecule drugs with metabolism-related drug-drug interactions (DDIs) and 320 unique food items, composed of 563 unique compounds with 179 health effects. The potential number of interactions is 87,192 and 92,143 when two different versions of the graph referred to as disjoint and joint graphs are considered, respectively. We defined several similarity subnetworks comprising food-drug similarity (FDS), drug-drug similarity (DDS), and food-food similarity (FFS) networks, based on similarity profiles. A unique part of the graph is the encoding of the food composition as a set of nodes and calculating a content contribution score to re-weight the similarity links. To predict new FDI links, we applied the path category-based (path length 2 and 3) and neighborhood-based similarity-based link prediction algorithms. We calculated the precision@top (top 1%, 2%, and 5%) of the newly predicted links, the area under the receiver operating characteristic curve, and precision-recall curve. We have performed three types of evaluations to benchmark results using different types of interactions. The shortest path-based method has achieved a precision 84%, 60% and 40% for the top 1%, 2% and 5% of FDIs identified, respectively. We validated the top FDIs predicted using FDMine to demonstrate its applicability and we relate therapeutic anti-inflammatory effects of food items informed by FDIs. We hypothesize that the proposed framework can be used to gain new insights on FDIs. FDMine is publicly available to support clinicians and researchers.

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

Cited by 24 publications

References 40 publications

Arabidopsis-Based Dual-Layered Biological Network Analysis Elucidates Fully Modulated Pathways Related to Sugarcane Resistance on Biotrophic Pathogen Infection

Arabidopsis-Based Dual-Layered Biological Network Analysis Elucidates Fully Modulated Pathways Related to Sugarcane Resistance on Biotrophic Pathogen Infection

Overlapping Structures Detection in Protein-Protein Interaction Networks Using Community Detection Algorithm Based on Neighbor Clustering Coefficient

FDMine: a graph mining approach to predict and evaluate food-drug interactions

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