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BaNDyT: Bayesian Network Modeling of Molecular Dynamics Trajectories.
J Chem Inf Model
January 2025
Department of Computational and Quantitative Medicine, Beckman Research Institute of the City of Hope, 1218 S 5th Ave, Monrovia, California 91016, United States.
Bayesian network modeling (BN modeling, or BNM) is an interpretable machine learning method for constructing probabilistic graphical models from the data. In recent years, it has been extensively applied to diverse types of biomedical data sets. Concurrently, our ability to perform long-time scale molecular dynamics (MD) simulations on proteins and other materials has increased exponentially.
View Article and Find Full Text PDFFunctional evolution of thyrotropin-releasing hormone neuropeptides: Insights from an echinoderm.
Zool Res
January 2025
The Key Laboratory of Mariculture, Ministry of Education, Ocean University of China, Qingdao, Shandong 266003, China. E-mail:
Feeding behavior is regulated by a complex network of endogenous neuropeptides. In chordates, this role is suggested to be under the control of diverse factors including thyrotropin-releasing hormone (TRH). However, whether this regulatory activity of TRH is functionally conserved in non-chordate metazoans, and to what extent this process is underpinned by interactions of TRH with other neuropeptides such as cholecystokinin (CCK, known as a satiety signal), remain unclear.
View Article and Find Full Text PDFDevelopment of the brain network control theory and its implications.
Psychoradiology
December 2024
Department of Psychology, University of Science and Technology of China, Hefei, Anhui 230062, China.
Brain network control theory (NCT) is a groundbreaking field in neuroscience that employs system engineering and cybernetics principles to elucidate and manipulate brain dynamics. This review examined the development and applications of NCT over the past decade. We highlighted how NCT has been effectively utilized to model brain dynamics, offering new insights into cognitive control, brain development, the pathophysiology of neurological and psychiatric disorders, and neuromodulation.
View Article and Find Full Text PDFDiscovering topics and trends in biosecurity law research: A machine learning approach.
One Health
June 2025
Murdoch University, Perth, WA 6150, Australia.
This study employed machine learning techniques, specifically Latent Dirichlet Allocation (LDA), to analyze 559 articles on biosecurity legislation from 1996 to 2023. The LDA model identified nine key research topics, including Agricultural Management and Production, Biosafety and Environmental Impact, Biological Invasion and Regulation, Biosecurity Legislation and Prevention, Agriculture and Environmental Relations, Virus Infection and Governance, Health Risk Assessment and Detection, Disease Prevention and Biotechnology, and Policy Control and Research. The findings reveal significant trends: an increasing focus on Biosecurity Legislation and Prevention and a declining interest in Agricultural Management and Production.
View Article and Find Full Text PDFHeterogeneous and higher-order cortical connectivity undergirds efficient, robust, and reliable neural codes.
iScience
January 2025
Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, 1202 6 Geneva, Switzerland.
We hypothesized that the heterogeneous architecture of biological neural networks provides a substrate to regulate the well-known tradeoff between robustness and efficiency, thereby allowing different subpopulations of the same network to optimize for different objectives. To distinguish between subpopulations, we developed a metric based on the mathematical theory of simplicial complexes that captures the complexity of their connectivity by contrasting its higher-order structure to a random control and confirmed its relevance in several openly available connectomes. Using a biologically detailed cortical model and an electron microscopic dataset, we showed that subpopulations with low simplicial complexity exhibit efficient activity.
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