Cardiovascular-kidney-metabolic syndrome (CKM) and its association with exposure to emerging pollutants, particularly perfluoroalkyl and polyfluoroalkyl substances (PFAS), present significant challenges for environmental public health and risk predic...
Predicting drug responses using genetic and transcriptomic features is crucial for enhancing personalized medicine. In this study, we implemented an ensemble of machine learning algorithms to analyze the correlation between genetic and transcriptomic...
Ecotoxicology and environmental safety
Sep 1, 2025
Groundwater quality monitoring is crucial for protecting the environment and human health. Machine learning (ML) offers substantial potential for enhancing groundwater quality prediction, classification, and identification of pollution indicators. Th...
BACKGROUND: Functional magnetic resonance imaging (fMRI) is a powerful tool for non-invasive evaluation of micro-changes in the kidneys. This study aims to develop classification and prognostic models based on multi-modal data.
Breast cancer, the most commonly diagnosed disease worldwide, has been linked to the overexpression of the kinesin Eg5 protein, a spindle motor protein crucial for the assembly and maintenance of the bipolar spindle during mitosis. This makes Eg5 an ...
Ecotoxicology and environmental safety
Sep 1, 2025
3-Monochloropropane-1,2-diol (3-MCPD) and glycidol along with their esters are commonly found in chemical production, wastewater treatment, food processing, and exhibit toxicity. Accurate exposure assessment is essential for evaluating the environmen...
BACKGROUND AND OBJECTIVE: Accurate staging of keratoconus (KC) is crucial for timely intervention and improving patient quality of life. Unlike prior studies that relied on traditional base machine learning (ML) models, this paper proposes a more adv...
Colorectal cancer (CRC) is a leading cause of death worldwide. A plethora of research shows the alteration of the gut microbiome and the association of bacterial taxa with CRC. Gaining insights into the health status through microbiome-based diagnosi...
OBJECTIVE: The heterogeneity of machine learning (ML) models predicting the risk of stroke-associated pneumonia (SAP) is considerable. This study aims to conduct a meta-analysis and comparison of published ML models that predict SAP risk.
This paper demonstrates an accurate and efficient methodology for fermentation contamination detection and reduction using two machine learning (ML) methods, including one-class support vector machine and autoencoders. We also optimize as many hyperp...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.