After uncovering a unifying algorithm that links more than 20 common machine-learning approaches, researchers organized them into a 'periodic table of machine learning' that can help scientists ...

Genome editing has advanced at a rapid pace with promising results for treating genetic conditions -- but there is always room for improvement. A new paper showcases the power of scalable protein ...

Here a machine learning algorithm will be trained to predict a liver disease in patients using a data-set collected from North East of Andhra Pradesh, India. Using machine learning models to predict ...

This study introduces a hybrid framework combining traditional Proportional-Integral-Derivative (PID) control with advanced machine learning to optimize AGV performance. A genetic algorithm (GA) was ...

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 ...

In 2017, Ezekiel Emanuel, a well-known oncologist and health policy commentator, said radiologists would soon be out of work thanks to machine learning. That hasn’t happened, but although ...

A machine-learning algorithm, catGRANULE 2.0 ROBOT, has been developed to predict the potential of proteins to form toxic aggregates linked to neurodegenerative diseases like ALS, Parkinson's, and ...

Add a description, image, and links to the genetic-algorithm-framework topic page so that developers can more easily learn about it.