Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered ...

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

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 ...

Effect of incorporating symptom burden with mortality as a composite outcome on accuracy and bias in palliative care identification algorithms in oncology. This is an ASCO Meeting Abstract from the ...

Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk ...

PLSKB: An Interactive Knowledge Base to Support Diagnosis, Treatment, and Screening of Lynch Syndrome on the Basis of Precision Oncology We used an innovative machine learning approach to analyze ...

A powerful artificial intelligence (AI) tool could give clinicians a head start in identifying life-threatening complications ...

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

Plants are constantly exposed to a wide array of biotic and abiotic stresses in their natural environments, posing ...

Yale researchers have developed a machine learning model, called Immunostruct, that can help scientists create more ...