Abstract: Large-scale, high-quality data are considered an essential factor for the successful application of many deep learning techniques. Meanwhile, numerous real-world deep learning tasks still ...

Abstract: The recent advancements in Machine Learning (ML) and Deep Learning (DL) provide a new dimension in biomedical big data analysis, while the cloud computing technologies present the ...

This study presents DeepTX, a valuable methodological tool that integrates mechanistic stochastic models with single-cell RNA sequencing data to infer transcriptional burst kinetics at genome scale.

Introduction: Recent advances in artificial intelligence have transformed the way we analyze complex environmental data. However, high-dimensionality, spatiotemporal variability, and heterogeneous ...

This review focuses on the recent advancements in neuroimaging enabled by deep learning techniques, specifically highlighting their applications in brain disorder detection and diagnosis. The ...

One significant challenge in applying deep learning to tabular data is balancing model complexity and computational efficiency. Traditional machine learning methods, particularly gradient-boosted ...

The integration of big data and deep learning in healthcare is rapidly transforming the landscape of medical research, diagnostics, and patient care. Our Research Topic, “Utilizing big data and deep ...

Income inequality is on the rise. The rich are getting better at passing their advantages on to their kids. Lifestyle and values gaps are widening between the educated and uneducated. So the big issue ...