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 ...

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

High-precision GNSS applications, such as real-time displacement monitoring and vehicle navigation, rely heavily on resolving carrier-phase ambiguities. However, traditional methods like the R-ratio ...

One of the current hot research topics is the combination of two of the most recent technological breakthroughs: machine learning and quantum computing. An experimental study shows that already ...

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

Across the U.S., hundreds of sites on land or in lakes and rivers are heavily contaminated with hazardous waste produced by human activity. Many of ...

Using machine learning models, researchers at Michigan Medicine have identified a potential way to diagnose amyotrophic lateral sclerosis, or ALS, earlier from a blood sample, a study suggests.

Electron density prediction for a four-million-atom aluminum system using machine learning, deemed to be infeasible using traditional DFT method. × Researchers from Michigan Tech and the University of ...

Lithium-ion batteries have become the quiet workhorses of the energy transition, but the way they are designed and tested has ...