Assessing the suitability of network community detection to available meta-data using rank stability

Hartman, Ryan; Faustino, Josemar; Pinheiro, Diego; Menezes, Ronaldo

doi:10.1145/3106426.3106493

Cited by 8 publications

(16 citation statements)

References 22 publications

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“…We disregarded the varied nature of these relationships and represented any interaction as an edge, which resulted in an undirected protein network (See edgelist supplementary file). Additionally, we applied a community detection method using Louvain modularity [60] to uncover a coarse-grained view of the network structure [61]. Network manipulation, community detection, and figure generation were performed using the software Gephi (version 0.9.2) [62].…”

Section: Materials and Methodsmentioning

confidence: 99%

Unveiling the Interplay between the TLR4/MD2 Complex and HSP70 in the Human Cardiovascular System: A Computational Approach

Oliveira

Faustino

Lima

et al. 2019

IJMS

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While precise mechanisms underlying cardiovascular diseases (CVDs) are still not fully understood, previous studies suggest that the innate immune system, through Toll-like receptor 4 (TLR4), plays a crucial part in the pathways leading to these diseases, mainly because of its interplay with endogenous molecules. The Heat-shock protein 70 family (HSP70-70kDa) is of particular interest in cardiovascular tissues as it may have dual effects when interacting with TLR4 pathways. Although the hypothesis of the HSP70 family members acting as TLR4 ligands is becoming widely accepted, to date no co-crystal structure of this complex is available and it is still unknown whether this process requires the co-adaptor MD2. In this study, we aimed at investigating the interplay between the TLR4/MD2 complex and HSP70 family members in the human cardiovascular system through transcriptomic data analysis and at proposing a putative interaction model between these proteins. We report compelling evidence of correlated expression levels between TLR4 and MD2 with HSP70 cognate family members, especially in heart tissue. In our molecular docking simulations, we found that HSP70 in the ATP-bound state presents a better docking score towards the TLR4/MD2 complex compared to the ADP-bound state (−22.60 vs. −10.29 kcal/mol, respectively). Additionally, we show via a proximity ligation assay for HSP70 and TLR4, that cells stimulated with ATP have higher formation of fluorescent spots and that MD2 might be required for the complexation of these proteins. The insights provided by our computational approach are potential scaffolds for future in vivo studies investigating the interplay between the TLR4/MD2 complex and HSP70 family members in the cardiovascular system.

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Section: Materials and Methodsmentioning

confidence: 99%

Unveiling the Interplay between the TLR4/MD2 Complex and HSP70 in the Human Cardiovascular System: A Computational Approach

Oliveira

Faustino

Lima

et al. 2019

IJMS

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“…The lack of a general definition of what a community should be is also mirrored in the existent heterogeneity of communities extracted by different community detection algorithms [6] and as such requires a comparison among community detection algorithms. Four commonly used algorithms were selected: Louvain Modularity, Infomap, Stochastic Block Model, and Speaker-listener Label Propagation.…”

Section: Community Detectionmentioning

confidence: 99%

“…detection comparisons from previous works [6], but also advocate for a further comparison among community detection algorithms. The comparison of HSA delineations (Fig.…”

mentioning

confidence: 94%

“…Yet, a comparative analysis of community detection algorithms, is still lacking for optimal network-based HSA delineation. Such comparative analysis is needed given the heterogeneity of communities extracted by different community detection algorithms [6]. Though such comparative analysis was previously provided for grouping hospitals [3], the underlying networks differ from HPDNs in two fundamental aspects: nodes were hospitals instead of geographical regions, and links were patients sharing between hospitals instead of the total number of hospital discharges.…”

Section: Introductionmentioning

confidence: 99%

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Network-Based Delineation of Health Service Areas: A Comparative Analysis of Community Detection Algorithms

Pinheiro

Hartman

Romero

et al. 2020

Complex Networks XI

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0000−0001−9300−7196] , Ryan Hartman 3[0000−0001−8044−5461] , Erick Romero 1[0000−0001−5456−3093] , Ronaldo Menezes 2[0000−0002−6479−6429] , and Martin Cadeiras 1[0000−0003−4545−2871]Abstract. A Health Service Area (HSA) is a group of geographic regions served by similar health care facilities. The delineation of HSAs plays a pivotal role in the characterization of health care services available in an area, enabling a better planning and regulation of health care services. Though Dartmouth HSAs have been the standard delineation for decades, previous work has recently shown an improved HSA delineation using a network-based approach, in which HSAs are the communities extracted by the Louvain algorithm in hospital-patient discharge networks. Given the existent heterogeneity of communities extracted by different community detection algorithms, a comparative analysis of community detection algorithms for optimal HSA delineation is lacking. In this work, we compared HSA delineations produced by community detection algorithms using a large-scale dataset containing different types of hospitalpatient discharges spanning a 7-year period in US. Our results replicated the heterogeneity among community detection algorithms found in previous works, the improved HSA delineation obtained by a network-based, and suggested that Infomap may be a more suitable community detection for HSA delineation since it finds a high number of HSAs with high localization index and a low network conductance.

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“…In the context of political networks defined by affiliation switches, a community means a group of parties sharing a higher rate of member exchange. There are many methods of community detection, however, few of them support directed and weighted networks (Hartman et al 2017). In this work, we applied the stochastic block model (SBM) for community detection (Peixoto 2014) because it satisfies both direction and edge weights.…”

Section: Structural Evidence Via Community Analysismentioning

confidence: 99%

A data-driven network approach for characterization of political parties’ ideology dynamics

et al. 2019

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Political ideology is a major social phenomena that plays a crucial role in the formation and dynamics of ideologically-aligned social groups. This alignment gives rise to some of the most powerful social structures in modern democracies, the political parties. Due to the influence and importance of political parties in society, estimating their ideology is an active topic in political science research. However ideology is a very subjective phenomena and existing quantitative methods for ideology estimation are susceptible to a variety estimation issues, specially when applied in multi-partisan systems. In this work, we developed a data-driven network model for political ideology estimation based on partisan relationships established from party-switching politicians. The model is applied to the Brazilian case and yields results consistent with existing literature in the Brazilian political scenario while addressing data scarcity issues in existing methodologies.

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Assessing the suitability of network community detection to available meta-data using rank stability

Cited by 8 publications

References 22 publications

Unveiling the Interplay between the TLR4/MD2 Complex and HSP70 in the Human Cardiovascular System: A Computational Approach

Unveiling the Interplay between the TLR4/MD2 Complex and HSP70 in the Human Cardiovascular System: A Computational Approach

Network-Based Delineation of Health Service Areas: A Comparative Analysis of Community Detection Algorithms

A data-driven network approach for characterization of political parties’ ideology dynamics

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