"The biggest risk is not taking any risk ... the only strategy that is guaranteed to fail is not taking risks," advised Mark Zuckerberg.Every story has a beginning. Every story has an element of risk.

A machine learning-driven framework accurately predicts MPA exposure and supports individualized dosing in childhood-onset LN.

Abstract: Epilepsy is a neurological condition because seizures occur at random and thus patients require automated and accurate detection mechanisms. The study design employs an evaluation of ...

Machine-learning models accurately pinpointed differences in immune responses in healthy controls and those living with HIV.

How people with compromised immune systems respond to vaccines is an important area of immunological research. A study led by York University has found that not only could machine-learning models ...

1 Department of Computer and Instructional Technologies Education, Gazi Faculty of Education, Gazi University, Ankara, Türkiye. 2 Department of Forensic Informatics, Institute of Informatics, Gazi ...

Support vector machines improve classification by mapping inseparable signals into higher-dimensional spaces. Random forest models, through ensemble decision trees, increase robustness against ...

Background Patients with heart failure (HF) frequently suffer from undetected declines in cardiorespiratory fitness (CRF), which significantly increases their risk of poor outcomes. However, current ...

Both approaches identified hemoglobin as one of the most significant predictors of CKD risk. Additional top-ranked features included blood urea, sodium levels, red blood cell count, potassium, and ...

Heat stress is widely recognized as a critical risk factor in livestock systems. Rising temperatures and humidity levels can ...

Machine learning enhances proteomics by optimizing peptide identification, structure prediction, and biomarker discovery.