More information: Xiaorui Su et al, Interpretable identification of cancer genes across biological networks via transformer-powered graph representation learning, Nature Biomedical Engineering (2025).

MIT researchers designed a computationally efficient algorithm for machine learning with symmetric data that also requires fewer data for training than conventional approaches. Their work could inform ...

TigerGraph, provider of a leading graph analytics platform, is introducing the TigerGraph ML (Machine Learning) Workbench—a powerful toolkit that enables data scientists to significantly improve ML ...

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 ...