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

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

A new study published in the journal Minerals sheds light on this sweeping shift. Titled Big Data and AI in Geoscience: From ...

Valinor Discovery ("Valinor") and Renew Biotechnologies ("Renew") today announced a strategic collaboration to generate the ...

The Polymathic AI team has released two massive datasets for training artificial intelligence models to tackle problems across scientific disciplines. The datasets include data from dozens of sources.

Datasets fuel AI models like gasoline (or electricity, as the case may be) fuels cars. Whether they're tasked with generating text, recognizing objects, or predicting a company's stock price, AI ...

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

The search space for protein engineering grows exponentially with complexity. A protein of just 100 amino acids has 20^100 possible variants-more combinations than atoms in the observable universe.

Machine learning models are usually complimented for their intelligence. However, their success mostly hinges on one fundamental aspect: data labeling for machine learning. A model has to get familiar ...