graphkernels: R and Python packages for graph comparison

Sugiyama, Mahito; Ghisu, M. Elisabetta; Llinares-López, Felipe; Borgwardt, Karsten

doi:10.1093/bioinformatics/btx602

Cited by 37 publications

(23 citation statements)

References 10 publications

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“…To assess the non-randomness of the constructed network, 1000 random networks with the same number of vertices and interactions as the OS-specific network were generated using the Erdos-Renyi model (19) in the igraph R package (Version 0.7.1; ) (20). The arithmetic average values of the shortest path distance and clustering coefficient were calculated.…”

Section: Methodsmentioning

confidence: 99%

Pathway and network analysis of genes related to osteoporosis

Guo

Han

Guo³

et al. 2019

Mol Med Report

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As a common degenerative disease, osteoporosis (OS) is characterized by reduced bone mass and microarchitectural deterioration of bone tissue. Both genetic and environmental factors are involved in OS development. To date, ~300 genes have been confirmed to be involved in the pathogenesis of OS, a large majority of which have been independently investigated. As OS is a polygenetic disease, a comprehensive analysis focusing on the biological functions and interactions of OS-related genes would provide valuable information. In this study, OS related research deposited in PubMed was retrieved and genes related to OS were catalogued. Pathways with an enriched biological function for these genes were extracted, and the crosstalk between the enriched pathways was analyzed. A comprehensive network was constructed, and a minimal network was extracted using the Steiner minimal network algorithm. In this study, a total of 294 genes in were retrieved from PubMed. Biological processes found to be enriched included those related to bone metabolism and the immune system. In total, 58 pathways were enriched. Furthermore, the comprehensive network consisting of 3,943 nodes and 7,976 edges was constructed, among which 631 nodes and 2,581 edges contributed to the OS-specific molecular network. In this network, in excess of 300 potential genes associated with OS and two modules were identified. Thus, this study provides a mechanistic insight into OS and suggests more than 300 potential OS-related genes for future research.

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Section: Methodsmentioning

confidence: 99%

Pathway and network analysis of genes related to osteoporosis

Guo

Han

Guo³

et al. 2019

Mol Med Report

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“…The approach is very general, as feature vectors can be defined in very different forms. A recent paper 48 , introducing an R/Python implementation, summarizes 14 different kernel types among the most popular ones: the majority of them are based, in different forms, on statistics on node/edge labels (thus they fall out of the scope of our work, as we do not assume labels on nodes/edges). Two of them are based on graphlet count, and the remaining on the comparison of random walks on the two graphs.…”

Section: Measuring the Distance Between Networkmentioning

confidence: 99%

Comparing methods for comparing networks

Tantardini¹,

Ieva

Tajoli

et al. 2019

Sci Rep

192

124

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With the impressive growth of available data and the flexibility of network modelling, the problem of devising effective quantitative methods for the comparison of networks arises. Plenty of such methods have been designed to accomplish this task: most of them deal with undirected and unweighted networks only, but a few are capable of handling directed and/or weighted networks too, thus properly exploiting richer information. In this work, we contribute to the effort of comparing the different methods for comparing networks and providing a guide for the selection of an appropriate one. First, we review and classify a collection of network comparison methods, highlighting the criteria they are based on and their advantages and drawbacks. The set includes methods requiring known node-correspondence, such as DeltaCon and Cut Distance, as well as methods not requiring a priori known node-correspondence, such as alignment-based, graphlet-based, and spectral methods, and the recently proposed Portrait Divergence and NetLSD. We test the above methods on synthetic networks and we assess their usability and the meaningfulness of the results they provide. Finally, we apply the methods to two real-world datasets, the European Air Transportation Network and the FAO Trade Network, in order to discuss the results that can be drawn from this type of analysis.

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“…We compared networks using the connected graphlet algorithm described by Shervashidze et al 44 that is provided in the graphkernels 1.4 R package 45 and obtained a similarity matrix of the networks. The connected graphlet algorithm measures similarity between 2 graphs (networks) by comparing the distribution of graphlets (subnetworks) within 2 networks rather than node and edge labels, and has been shown to give competitive performance on unlabeled networks 44 .…”

Section: Methodsmentioning

confidence: 99%

Evaluation of multidisciplinary collaboration in pediatric trauma care using EHR data

Durojaiye

Levin

Toerper

et al. 2019

Journal of the American Medical Informatics Association

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Objectives The study sought to identify collaborative electronic health record (EHR) usage patterns for pediatric trauma patients and determine how the usage patterns are related to patient outcomes. Materials and Methods A process mining–based network analysis was applied to EHR metadata and trauma registry data for a cohort of pediatric trauma patients with minor injuries at a Level I pediatric trauma center. The EHR metadata were processed into an event log that was segmented based on gaps in the temporal continuity of events. A usage pattern was constructed for each encounter by creating edges among functional roles that were captured within the same event log segment. These patterns were classified into groups using graph kernel and unsupervised spectral clustering methods. Demographics, clinical and network characteristics, and emergency department (ED) length of stay (LOS) of the groups were compared. Results Three distinct usage patterns that differed by network density were discovered: fully connected (clique), partially connected, and disconnected (isolated). Compared with the fully connected pattern, encounters with the partially connected pattern had an adjusted median ED LOS that was significantly longer (242.6 [95% confidence interval, 236.9–246.0] minutes vs 295.2 [95% confidence, 289.2–297.8] minutes), more frequently seen among day shift and weekday arrivals, and involved otolaryngology, ophthalmology services, and child life specialists. Discussion The clique-like usage pattern was associated with decreased ED LOS for the study cohort, suggesting greater degree of collaboration resulted in shorter stay. Conclusions Further investigation to understand and address causal factors can lead to improvement in multidisciplinary collaboration.

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graphkernels: R and Python packages for graph comparison

Cited by 37 publications

References 10 publications

Pathway and network analysis of genes related to osteoporosis

Pathway and network analysis of genes related to osteoporosis

Comparing methods for comparing networks

Evaluation of multidisciplinary collaboration in pediatric trauma care using EHR data

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