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 ...

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A machine learning algorithm developed by Cambridge scientists was able to correctly identify in 97 cases out of 100 whether or not an individual had coeliac disease based on their biopsy, new ...