Evolutionary graph theory has grown to be an area of intense study. Despite the amount of interest in the field, it seems to have grown separate from other subfields of population genetics and evolution. In the current work I introduce the concept of Fisher's (1930) reproductive value into the study of evolution on graphs. Reproductive value is a measure of the expected genetic contribution of an individual to a distant future generation. In a heterogeneous graph-structured population, differences in the number of connections among individuals translate into differences in the expected number of offspring, even if all individuals have the same fecundity. These differences are accounted for by reproductive value. The introduction of reproductive value permits the calculation of the fixation probability of a mutant in a neutral evolutionary process in any graph-structured population for either the moran birth-death or death-birth process.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.jtbi.2013.09.032 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Managing human-AI collaborations within Industry 5.0 scenarios via knowledge graphs: key challenges and lessons learned.
Front Artif Intell
November 2024
Software Competence Center Hagenberg (SCCH), Hagenberg, Austria.
Article Synopsis
- - The paper explores how Knowledge Graphs (KGs) can facilitate real-time human interventions in AI-driven manufacturing processes in the evolving Industry 5.0 environment to enhance system performance under dynamic conditions.
- - It advocates for a "late shaping" design approach, allowing for adaptability and integration of human intelligence during runtime, contrasting with the traditional "early shaping" method that fixes system behavior at the design phase.
- - The discussion includes insights from the European project Teaming.AI, focusing on challenges such as domain expertise modeling, vertical knowledge integration, and dynamically populating KGs for improved relational machine learning outcomes.
An Online Multimodal Food Data Exploration Platform for Specific Population Health: Development Study.
JMIR Form Res
November 2024
Institute of Medical Information and Library, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing, China.
Background: Nutrient needs vary over the lifespan. Improving knowledge of both population groups and care providers can help with healthier food choices, thereby promoting population health and preventing diseases. Providing evidence-based food knowledge online is credible, low cost, and easily accessible.
View Article and Find Full Text PDFA Semantic Knowledge Graph of European Mountain Value Chains.
Sci Data
September 2024
Dipartimento di Scienze Agrarie, Alimentari e Agro-ambientali, dell'Università di Pisa, Pisa, 56124, Italy.
The United Nations forecast a significant shift in global population distribution by 2050, with rural populations projected to decline. This decline will particularly challenge mountain areas' cultural heritage, well-being, and economic sustainability. Understanding the economic, environmental, and societal effects of rural population decline is particularly important in Europe, where mountainous regions are vital for supplying goods.
View Article and Find Full Text PDFCompetition for resources can reshape the evolutionary properties of spatial structure.
bioRxiv
April 2024
Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.
Article Synopsis
- Many ecosystems can be modeled as networks where nodes are individuals or groups and links represent their interactions.
- Previous evolutionary models on networks didn't consider the differences in ecological niches and their effects.
- This study merges resource competition theory with evolutionary graph theory and finds that ecological competition can change the influence of networks on the evolution of harmful mutants, revealing complex relationships based on niche overlap.
Supervised contrastive learning enhances graph convolutional networks for predicting neurodevelopmental deficits in very preterm infants using brain structural connectome.
Neuroimage
May 2024
Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Artificial Intelligence Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Computer Science, University of Cincinnati, Cincinnati, OH, USA; Department of Biomedical Engineering, University of Cincinnati, Cincinnati, OH, USA; Department of Biomedical Informatics, University of Cincinnati College of Medicine, Cincinnati, OH, USA; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA. Electronic address:
Very preterm (VPT) infants (born at less than 32 weeks gestational age) are at high risk for various adverse neurodevelopmental deficits. Unfortunately, most of these deficits cannot be accurately diagnosed until the age of 2-5 years old. Given the benefits of early interventions, accurate diagnosis and prediction soon after birth are urgently needed for VPT infants.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!