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

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

A Hybrid Machine Learning Framework for Early Diabetes Prediction in Sierra Leone Using Feature Selection and Soft-Voting Ensemble ...

How people with compromised immune systems respond to vaccines is an important area of immunological research. A study led by ...

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

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

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 ...

Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk identification to improve prevention and management strategies. Traditional risk ...

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