network diversity
Recently Published Documents


TOTAL DOCUMENTS

153
(FIVE YEARS 37)

H-INDEX

19
(FIVE YEARS 2)

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Zixin Shu ◽  
Jingjing Wang ◽  
Hailong Sun ◽  
Ning Xu ◽  
Chenxia Lu ◽  
...  

AbstractSymptom phenotypes have continuously been an important clinical entity for clinical diagnosis and management. However, non-specificity of symptom phenotypes for clinical diagnosis is one of the major challenges that need be addressed to advance symptom science and precision health. Network medicine has delivered a successful approach for understanding the underlying mechanisms of complex disease phenotypes, which will also be a useful tool for symptom science. Here, we extracted symptom co-occurrences from clinical textbooks to construct phenotype network of symptoms with clinical co-occurrence and incorporated high-quality symptom-gene associations and protein–protein interactions to explore the molecular network patterns of symptom phenotypes. Furthermore, we adopted established network diversity measure in network medicine to quantify both the phenotypic diversity (i.e., non-specificity) and molecular diversity of symptom phenotypes. The results showed that the clinical diversity of symptom phenotypes could partially be explained by their underlying molecular network diversity (PCC = 0.49, P-value = 2.14E-08). For example, non-specific symptoms, such as chill, vomiting, and amnesia, have both high phenotypic and molecular network diversities. Moreover, we further validated and confirmed the approach of symptom clusters to reduce the non-specificity of symptom phenotypes. Network diversity proposes a useful approach to evaluate the non-specificity of symptom phenotypes and would help elucidate the underlying molecular network mechanisms of symptom phenotypes and thus promotes the advance of symptom science for precision health.


PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0251878
Author(s):  
Eric Garcia ◽  
Daniel Wright ◽  
Remy Gatins ◽  
May B. Roberts ◽  
Hudson T. Pinheiro ◽  
...  

A common way of illustrating phylogeographic results is through the use of haplotype networks. While these networks help to visualize relationships between individuals, populations, and species, evolutionary studies often only quantitatively analyze genetic diversity among haplotypes and ignore other network properties. Here, we present a new metric, haplotype network branch diversity (HBd), as an easy way to quantifiably compare haplotype network complexity. Our metric builds off the logic of combining genetic and topological diversity to estimate complexity previously used by the published metric haplotype network diversity (HNd). However, unlike HNd which uses a combination of network features to produce complexity values that cannot be defined in probabilistic terms, thereby obscuring the values’ implication for a sampled population, HBd uses frequencies of haplotype classes to incorporate topological information of networks, keeping the focus on the population and providing easy-to-interpret probabilistic values for randomly sampled individuals. The goal of this study is to introduce this more intuitive metric and provide an R script that allows researchers to calculate diversity and complexity indices from haplotype networks. A group of datasets, generated manually (model dataset) and based on published data (empirical dataset), were used to illustrate the behavior of HBd and both of its terms, haplotype diversity, and a new index called branch diversity. Results followed a predicted trend in both model and empirical datasets, from low metric values in simple networks to high values in complex networks. In short, the new combined metric joins genetic and topological diversity of haplotype networks, into a single complexity value. Based on our analysis, we recommend the use of HBd, as it makes direct comparisons of network complexity straightforward and provides probabilistic values that can readily discriminate situations that are difficult to resolve with available metrics.


2021 ◽  
pp. 1532673X2110135
Author(s):  
Seong Jae Min

A survey of 3,441 U.S. social media users showed that a high portion believes in conspiracy theories, and their beliefs vary widely along the party lines and socio-demographic factors. In particular, conservative conspiracy theories were more pronounced than liberal ones, and older White males with high conservatism and Protestantism showed higher endorsement of conservative conspiracy theories. Furthermore, ideological conservatives who frequently discuss politics showed higher association with a conservative conspiracy theory than conservatives who discuss politics less frequently. However, network diversity moderated the interaction of conservative ideology and political discussion such that conservatives who discuss politics frequently in a relatively heterogeneous social media network setting had lower beliefs in a conspiracy theory than conservatives who do so in a more homogeneous network.


2021 ◽  
Vol 73 (2) ◽  
pp. 243-274
Author(s):  
Alexandra A. Siegel ◽  
Jonathan Nagler ◽  
Richard Bonneau ◽  
Joshua A. Tucker

abstractDo online social networks affect political tolerance in the highly polarized climate of postcoup Egypt? Taking advantage of the real-time networked structure of Twitter data, the authors find that not only is greater network diversity associated with lower levels of intolerance, but also that longer exposure to a diverse network is linked to less expression of intolerance over time. The authors find that this relationship persists in both elite and non-elite diverse networks. Exploring the mechanisms by which network diversity might affect tolerance, the authors offer suggestive evidence that social norms in online networks may shape individuals’ propensity to publicly express intolerant attitudes. The findings contribute to the political tolerance literature and enrich the ongoing debate over the relationship between online echo chambers and political attitudes and behavior by providing new insights from a repressive authoritarian context.


2021 ◽  
Author(s):  
Mark Lutter ◽  
Linus Weidner

This study examines how network characteristics affect the creative success of teams in the board game industry. Based on prior research, we argue that the degree of newcomers within a team affects the creative success negatively, while betweenness centrality affects creative team success positively. We test these assumptions with unique data on teams in the board game industry using two different measures of creative success (awards as well as user- ratings). The data covers 12,731 teams over a timeframe from 1951-2017. Results indicate moderately curvilinear effects of betweenness centrality as well as negative effects of the number of newcomers per team on creative success, suggesting that creativity is not only driven by network diversity, but also by trust and experience. This paper contributes to a better understanding of team-based creativity which also validates and expands on the prior knowledge of network-based origins of creativity in an industry-setting that has never been studied before.


Sign in / Sign up

Export Citation Format

Share Document