Machine learning interprets gene regulation

In this age of "big data," artificial intelligence (AI) has become a valuable ally for scientists. Machine learning algorithms, for instance, are helping biologists make sense of the dizzying number of molecular signals that control how genes function. But as new algorithms are developed to analyze even more data, they also become more complex and more difficult to interpret.

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.

A new brand of artificial neural network has solved an interpretability problem that has frustrated biologists. With it, scientists may solve mysteries about gene regulation and drug discovery.

See:

Ammar Tareen, Justin B. Kinney. Biophysical models of cis-regulation as interpretable neural networks. submitted to bioRxiv, 2019

Posted by Dr. Tim Sandle, Pharmaceutical Microbiology Resources (http://www.pharmamicroresources.com/)