One of the shared, fundamental goals of most chemistry researchers is the need to predict a molecule's properties, such as ...

A machine learning model bests traditional methods for predicting cirrhosis mortality among hospitalized patients.

More information: Taichi Masuda et al, Neural network ensembles for band gap prediction, Computational Materials Science (2024). DOI: 10.1016/j.commatsci.2024.113327 Provided by Kyoto University ...

The project I was a part of, Tectonic, was using machine learning to advance earthquake prediction. The European Research Council was sufficiently convinced of its potential to award it a four ...

To mitigate this challenge, we investigate how Machine Learning (ML) techniques, including Extreme Gradient Boosting (XGBoost), Convolutional Neural Network (CNN), and Graph Neural Network (GNN) can ...

The study reported that Predict+ was deemed fair for 98.6% of regression predictions, 99.4% of substantial clinical benefit (SCB) classification predictions, and 100% of minimal clinically ...

Machine learning models trained on tabular data exhibit a 76% accuracy for the random forest model at predicting relapse evaluated with a 10-fold cross-validation (the model was trained 10 times with ...

Researchers employed a machine learning technique known as random forest analysis and found that it significantly outperformed traditional methods in predicting which hospitalized patients with ...