Long-read sequencing technologies analyze long, continuous stretches of DNA. These methods have the potential to improve ...

An international research team, led by Jian-Feng Mao, has developed PlantLncBoost, a new computational tool that helps to ...

It can be relatively cheap to gather a lot of bio-signal data. To teach a machine-learning algorithm to find a relationship between bio-signals and health outcomes, however, you need to teach the ...

The first available use case for BeeKeeper, Mount Sinai and Morehouse is for chronic heart failure (CHF). AI model developers ...

discuss some of the most common machine learning algorithms, and explain how those algorithms relate to the other pieces of the puzzle of creating predictive models from historical data.

Bioinformatics for example ... Although deep learning and machine learning algorithms have existed for many years, the compute power wasn’t available to run wide datasets in parallel, in any sort of ...

Molecular machine learning (ML) underpins critical workflows in drug discovery, material science, and catalyst optimization by rapidly predicting molecular interactions and properties. For instance, ...