Genetic algorithms (GAs) have emerged as a powerful optimisation tool in product design and user-centred engineering, drawing inspiration from the principles of biological evolution. They use ...

This course covers three major algorithmic topics in machine learning. Half of the course is devoted to reinforcement learning with the focus on the policy gradient and deep Q-network algorithms. The ...

Machine learning is helping neuroscientists organize vast quantities of cells’ genetic data in the latest neurobiological cartography effort.

A Stanford-led study published in Nature on Feb. 26 found that age-related changes witnessed in diseases like Alzheimer’s may be related to a relatively untapped area of research in the brain. The ...

Systems controlled by next-generation computing algorithms could give rise to better and more efficient machine learning products, a new study suggests. Systems controlled by next-generation computing ...

For many diseases and chronic conditions, an individual's genes play a role in their likelihood of developing the disease. While some inherited diseases, such as cystic fibrosis or sickle cell anemia, ...

To prevent algorithmic bias, the authors call for multivariable modeling frameworks that jointly incorporate biological sex, genetic ancestry, and gender-related life-course exposures.

Machine learning is a subfield of artificial intelligence, which explores how to computationally simulate (or surpass) humanlike intelligence. While some AI techniques (such as expert systems) use ...