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Abstract Background Terpenes and phenolic compounds are multifunctional plant metabolites that contribute to defense, signaling, and ecological interactions, while supporting industrial applications. Here, we implemented...

Abstract Background Neonatal seizures are sudden, abnormal brain activities that occur during the neonatal period, are the most common neurological emergencies in newborns, and are associated with high morbidity and mort...

Abstract DNA methylation quantification in age-prediction models relies on only a few techniques with inherent limitations and biases, reducing the accuracy and reproducibility. We developed a new droplet digital PCR ass...

Abstract Graph Neural Networks (GNNs) are powerful tools for predicting chemical shifts in Nuclear Magnetic Resonance (NMR) spectroscopy. In this paper, we improve the state-of-the-art mean absolute error (MAE) on the Il...

Abstract Background Knee osteoarthritis (OA) is a leading global cause of disability, yet conventional tools lack sensitivity for early detection and precise prognostication. Artificial intelligence (AI) and machine lear...

Abstract Background Reliable identification of protein‑protein interactions (PPIs) is crucial for deciphering cellular functional networks. Current research models still face limitations in aligning heterogeneous featu...

Abstract Background Quantitative structure-activity relationship (QSAR) models are central to computer-aided drug discovery and predictive toxicology, but practical adoption is often impeded by ad-hoc tooling, inconsiste...

Abstract Background Colorectal signet ring cell carcinoma (CSRCC) is a rare subtype of colorectal cancer characterized by an exceptionally poor prognosis. Currently, accurate survival prediction models for CSRCC are lack...

Abstract Background Malaria continues to pose a major public health challenge in sub-Saharan Africa (SSA), where more than 95% of global cases and deaths occur. Despite WHO Global Technical Strategy for Malaria (GTS) tar...

Abstract Background Organizing free-text patient data into a structured format is labor-intensive and time-consuming. This study aims to evaluate the effectiveness of a fine-tuned lightweight language model in structurin...

Abstract Background Accurate and timely estimation of nitrogen nutrition index (NNI) is critical for assessing crop nitrogen (N) status and implementing precision N management. While machine learning (ML) techniques comb...

Abstract Background Surgical site infections (SSIs) continue to exert a substantial burden on healthcare systems, particularly in resource-limited settings where they contribute to prolonged hospitalizations, escalated c...

Abstract Background Interstitial lung disease (ILD) is characterized by marked heterogeneity and an overall poor prognosis, with many patients experiencing rapid progression and high short-term mortality. Accurate early ...

Abstract Humans are exposed to thousands of chemicals throughout their life. Many of these chemicals are detected in blood and have been catalogued in the Blood Exposome Database. Comprehensive hazard assessment of a che...