scholarly journals Multilayer network analysis unravels haulage vehicles as a hidden threat to the British swine industry

2020 ◽  
Vol 67 (3) ◽  
pp. 1231-1246 ◽  
Author(s):  
Thibaud Porphyre ◽  
Barend M. de C. Bronsvoort ◽  
George J. Gunn ◽  
Carla Correia‐Gomes
Keyword(s):  
Network Analysis ◽  
Swine Industry ◽  
Multilayer Network

scholarly journals Survey methods for seed system network analysis

2017 ◽  
Author(s):  
Christopher E Buddenhagen ◽  
Kelsey F Andersen ◽  
James C Fulton ◽  
Karen A Garrett
Keyword(s):  
Statistical Analysis ◽  
Network Analysis ◽  
Survey Methods ◽  
Seed Systems ◽  
Present Survey ◽  
Multilayer Network ◽  
Network Analyses ◽  
Variety Selection ◽  
Seed System ◽  
System Success

We present survey questions useful for describing agricultural seed systems. The questions are designed so that they can be used for standardized comparisons among seed systems, addressing both networks for seed movement and networks for the communication of information related to variety selection and integrated pest management. This approach provides information that can be used in multilayer network analyses of how information influences seed system success. Also provided are example data sheets with field descriptors that should provide for straightforward statistical analysis after data collection.

scholarly journals Multilayer Network Analysis of Oil Linkages

2018 ◽  
Author(s):  
Roberto Casarin ◽  
Enrique ter Horst ◽  
German Molina ◽  
Ramon Espinasa ◽  
Carlos Sucre ◽  
...  
Keyword(s):  
Network Analysis ◽  
Multilayer Network

scholarly journals Multilayer Network Analysis for Improved Credit Risk Prediction

Omega ◽  
2021 ◽  
pp. 102520
Author(s):  
María Óskarsdóttir ◽  
Cristián Bravo
Keyword(s):  
Network Analysis ◽  
Credit Risk ◽  
Risk Prediction ◽  
Multilayer Network

scholarly journals The use of multilayer network analysis in animal behaviour

2019 ◽  
Vol 149 ◽  
pp. 7-22 ◽  
Author(s):  
Kelly R. Finn ◽  
Matthew J. Silk ◽  
Mason A. Porter ◽  
Noa Pinter-Wollman
Keyword(s):  
Network Analysis ◽  
Animal Behaviour ◽  
Multilayer Network

scholarly journals The Network Theory of Psychiatric Disorders: A Critical Assessment of the Inclusion of Environmental Factors

2021 ◽  
Vol 12 ◽  
Author(s):  
Nina S. de Boer ◽  
Leon C. de Bruin ◽  
Jeroen J. G. Geurts ◽  
Gerrit Glas
Keyword(s):  
Network Analysis ◽  
Environmental Factors ◽  
Psychiatric Disorders ◽  
Network Theory ◽  
Mechanistic Explanation ◽  
Critical Assessment ◽  
Multilayer Network ◽  
Network Characteristics ◽  
Causal Mechanisms ◽  
Insight Into

Borsboom and colleagues have recently proposed a “network theory” of psychiatric disorders that conceptualizes psychiatric disorders as relatively stable networks of causally interacting symptoms. They have also claimed that the network theory should include non-symptom variables such as environmental factors. How are environmental factors incorporated in the network theory, and what kind of explanations of psychiatric disorders can such an “extended” network theory provide? The aim of this article is to critically examine what explanatory strategies the network theory that includes both symptoms and environmental factors can accommodate. We first analyze how proponents of the network theory conceptualize the relations between symptoms and between symptoms and environmental factors. Their claims suggest that the network theory could provide insight into the causal mechanisms underlying psychiatric disorders. We assess these claims in light of network analysis, Woodward’s interventionist theory, and mechanistic explanation, and show that they can only be satisfied with additional assumptions and requirements. Then, we examine their claim that network characteristics may explain the dynamics of psychiatric disorders by means of a topological explanatory strategy. We argue that the network theory could accommodate topological explanations of symptom networks, but we also point out that this poses some difficulties. Finally, we suggest that a multilayer network account of psychiatric disorders might allow for the integration of symptoms and non-symptom factors related to psychiatric disorders and could accommodate both causal/mechanistic and topological explanations.

scholarly journals Multilayer network analysis of miRNA and protein expression profiles in breast cancer patients

2018 ◽  
Author(s):  
Yang Zhang ◽  
Jiannan Chen ◽  
Dehua Wang ◽  
Weihui Cong ◽  
Bo Shiun Lai ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Network Analysis ◽  
Complex Network ◽  
Expression Profiles ◽  
Machine Learning Algorithms ◽  
Cancer Tissue ◽  
Breast Cancer Patients ◽  
Multilayer Network ◽  
Functional Relationships ◽  
Maximal Information Coefficient

AbstractMiRNAs and proteins play important roles in different stages of tumor development and serve as biomarkers for the early diagnosis of cancer. A new algorithm that combines machine learning algorithms and multilayer complex network analysis is hereby proposed to explore the potential diagnostic values of miRNAs and proteins. XGBoost and random forest algorithms were employed to exclude unrelated miRNAs and proteins, and the most significant candidates were retained for the further analysis. Given these candidates’ possible functional relationships to one other, a multilayer complex network was constructed to identify miRNAs and proteins that could serve as biomarkers for breast cancer. Proteins and miRNAs that are nodes in the network were subsequently categorized into two network layers considering their distinct functions. Maximal information coefficient (MIC) was applied to assess intralayer and interlayer connection. The betweenness centrality was used as the first measurement of the importance of the nodes within each single layer. To further characterize the interlayer interaction between miRNAs and proteins, the degree of the nodes was chosen as the second measurement to map their signalling pathways. By combining these two measurements into one score and comparing the difference of the same candidate between normal tissue and cancer tissue, this novel multilayer network analysis could be applied to successfully identify molecules associated with breast cancer.

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