TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
Jan 10, 2026
This study presents a Bayesian neural networks framework with LASSO regularization and the GSMeSP interpretability tool, enabling accurate, uncertainty-aware, and biologically interpretable genomic prediction. Deep learning offers significant potenti...
Proceedings of the National Academy of Sciences of the United States of America
Jan 9, 2026
The human brain excels at complex tasks with remarkable efficiency, adaptability, and resilience, making it a powerful source of inspiration for AI. Here, we present a neural dynamics model inspired by the primate dorsal visual pathway, a circuit cru...
Recent advances in artificial intelligence (AI) and machine learning (ML) are revolutionizing nanopharmaceutical development by enabling data-driven formulation design, process optimization, and prediction of biological performance. AI encompasses co...
Tuberculosis (TB) remains a major global health challenge, causing approximately 1.4 million deaths annually. In many high-burden regions, limited access to expert radiological interpretation leads to delayed or missed diagnoses. To address this, we ...
Tabular data is the predominant format for statistical analysis and machine learning across domains such as finance, biomedicine, and environmental sciences. However, conventional methods often face challenges when dealing with high dimensionality an...
Fatigue-induced degradation in Carbon Fiber Reinforced Polymer (CFRP) structures poses a critical challenge in long-term structural health monitoring (SHM) applications. In this study, a hybrid deep learning framework is proposed for fatigue state cl...
Accurate prediction of the compression deformation coefficient of loess fillers is key for stability assessment of loess subgrade engineering. In this study, the effects of key influencing factors such as compaction, water content, vertical pressure ...
This study addresses the important question of how neuron model choice and learning rules shape the classification performance of Spiking Neural Networks (SNNs) in bio-signal processing. By systematically contrasting Leaky Integrate-and-Fire, metaneu...
Image fusion is a challenging task that aims to generate a composite image by combining information from diverse sources. While deep learning (DL) algorithms have achieved promising results, most rely on complex encoders or attention mechanisms, lead...
The neocortex is composed of spiking neurons interconnected in a sparse, recurrent network. Spiking activity within these networks underlies the computations that transform sensory inputs into appropriate behavioral responses. In this study, we train...
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