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BackgroundProstate cancer is the sixth most common cause of cancer-related death in males globally and is often identified as a secondary malignancy. Early detection and appropriate management increase the patient’s li...

Abstract Accurate and real-time prediction of lost circulation is essential for ensuring drilling safety and operational efficiency. Existing data-driven approaches, however, often lack effective integration of domain ex...

Using historical data from blasting operations of open-pit mines to predict the average rock fragment size is crucial for optimizing blasting parameters. However, existing methods face 3 major challenges: interference fr...

The aim of this study was to develop equations for predicting empty body weight and economically important carcass characteristics using production indices for sheep finished in confinement. A total of 258 Dorper x Santa...

Heart disease is rapidly increasing. For early and accurate prediction, advanced machine learning (ML) models are needed. However, the sensitive nature of clinical data, along with strict regulatory constraints, creates ...

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...

This study addresses the limitations of deterministic constitutive models for polymer foams, which often fail to capture intrinsic material variability. It introduces a framework to quantify the predictive uncertainty of...

The sustainable utilization of post-consumer waste glass in concrete has emerged as a promising approach to reduce cement consumption, mitigate landfill disposal, and enhance material performance. However, most previous ...

Prediction invariance of causal models under heterogeneous settings has been exploited by a number of recent methods for causal discovery, typically focussing on recovering the causal parents of a target variable of inte...

Transformer-based protein language models (PLMs) learn meaningful representations from millions of unlabeled sequences, capturing evolutionary patterns and functional relationships. Recent advances include ESM-2’s syst...

The rapid expansion of whole-genome sequencing (WGS) has highlighted the important contribution of noncoding variants to human disease, yet their pathogenic mechanisms remain difficult to resolve. Traditional statistical...

Abstract In the world of the Internet of Things (IoT), wireless sensor networks (WSNs) play a vital role in enabling seamless communication between connected devices. In a WSN data prediction system based, clustering and...

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...

BackgroundChildhood obesity represents a significant global public health challenge. Accurate and rapid prediction models for identifying obesity risk in children and adolescents are essential for facilitating early prev...

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 Traffic flow prediction is a fundamental building block for Intelligent Transportation Systems (ITS), enabling proactive congestion mitigation, adaptive traffic signal control, and reliable route guidance. Howev...

Abstract Cardiovascular disease (CVD) is one of the leading causes of death worldwide, early and accurate prediction is crucial for reducing both incidence and mortality rates. Aiming at the problems of insufficient feat...

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 Advances in AI have transformed the traditional Internet of Things (IoT) into smarter, more versatile next-generation devices. Enhanced connectivity among sensors, actuators, and appliances boosts data availabil...

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 To address the limitations of existing oil-well production forecasting models, particularly suboptimal hyperparameter selection, difficulties in fine-tuning, insufficient global search capability, and slow conve...

BackgroundThe prevalence of chronic diseases among the Chinese occupational population is rising steadily, with hypertension and diabetes becoming important health concerns. Occupational health examinations (OHE) provide...

Customer churn prediction is essential across data-driven industries such as insurance, digital banking, e-commerce, and subscription platforms, where retaining existing customers is typically more cost-effective than ac...

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...