Understanding Homophily and More-Becomes-More Through Adaptive Temporal-Causal Network Models

2017 ◽  
pp. 16-29 ◽  
Author(s):  
Sven van den Beukel ◽  
Simon H. Goos ◽  
Jan Treur
Keyword(s):  
Network Models ◽  
Causal Network

Causal Network Models in Expert Systems

1994 ◽  
Vol 9 (5) ◽  
pp. 315-328 ◽  
Author(s):  
E. Castillo ◽  
A. Cobo ◽  
J. M. Gutiérrez ◽  
A. Iglesias ◽  
H. Sagástegui
Keyword(s):  
Expert Systems ◽  
Network Models ◽  
Causal Network

scholarly journals Multiscale causal network models of Alzheimer’s disease identify VGF as a key regulator of disease

2018 ◽  
Author(s):  
Noam D. Beckmann ◽  
Wei-Jye Lin ◽  
Minghui Wang ◽  
Ariella T. Cohain ◽  
Pei Wang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Network Models ◽  
Protective Role ◽  
Complex Nature ◽  
Causal Network ◽  
Dna Variation ◽  
Gene And Protein Expression ◽  
Significant Enrichment ◽  
5Xfad Mice

AbstractThough discovered over 100 years ago, the molecular foundation of sporadic Alzheimer’s disease (AD) remains elusive. To elucidate its complex nature, we constructed multiscale causal network models on a large human AD multi-omics dataset, integrating clinical features of AD, DNA variation, and gene and protein expression into probabilistic causal models that enabled detection and prioritization of high-confidence key drivers of AD, including the top predicted key driver VGF. Overexpression of neuropeptide precursor VGF in 5xFAD mice partially rescued beta-amyloid-mediated memory impairment and neuropathology. Molecular validation of network predictions downstream of VGF was achieved, with significant enrichment for homologous genes identified as differentially expressed in 5xFAD brains overexpressing VGF versus controls. Our findings support a causal and/or protective role for VGF in AD pathogenesis and progression.One sentence summaryVGF protects against Alzheimer’s disease

Quantifying the Biological Impact of Active Substances Using Causal Network Models

2015 ◽  
pp. 223-256 ◽  
Author(s):  
Alain Sewer ◽  
Florian Martin ◽  
Walter K. Schlage ◽  
Julia Hoeng ◽  
Manuel C. Peitsch
Keyword(s):  
Network Models ◽  
Causal Network ◽  
Biological Impact ◽  
Active Substances

What's in a model? Network models as tools instead of representations of what psychiatric disorders really are

2019 ◽  
Vol 42 ◽  
Author(s):  
Hanna M. van Loo ◽  
Jan-Willem Romeijn
Keyword(s):  
Psychiatric Disorders ◽  
Network Models ◽  
Model Network

AbstractNetwork models block reductionism about psychiatric disorders only if models are interpreted in a realist manner – that is, taken to represent “what psychiatric disorders really are.” A flexible and more instrumentalist view of models is needed to improve our understanding of the heterogeneity and multifactorial character of psychiatric disorders.

Special, radical, failure of reduction in psychiatry

2019 ◽  
Vol 42 ◽  
Author(s):  
Don Ross
Keyword(s):  
Mental Disorder ◽  
Brain Structure ◽  
Causal Structure ◽  
Network Models ◽  
Gambling Addiction ◽  
Brain Disorder ◽  
Mental Processes ◽  
Special Radical ◽  
Intentional Relationships

AbstractUse of network models to identify causal structure typically blocks reduction across the sciences. Entanglement of mental processes with environmental and intentional relationships, as Borsboom et al. argue, makes reduction of psychology to neuroscience particularly implausible. However, in psychiatry, a mental disorder can involve no brain disorder at all, even when the former crucially depends on aspects of brain structure. Gambling addiction constitutes an example.

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