Machine Learning Discovers Potential new Tuberculosis Drugs

#artificialintelligence 

Many biologists use machine learning (ML) as a computational tool to analyze a massive amount of data, helping them to recognise potential new drugs. MIT researchers have now integrated a new feature into these types of machine learning algorithms, enhancing their prediction-making ability. Using this new tool allows computer models to account for uncertainty in the data they are testing, MIT researchers detected several promising components that target a protein required by the bacteria that cause tuberculosis (TB). Although computer scientists previously used this technique, they have not taken off in biology. "It could also prove useful in protein design and many other fields of biology," says the Simons Professor of Mathematics and head of the Computation and Biology group in MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) Bonnie Berger.

Duplicate Docs Excel Report

Title
None found

Similar Docs  Excel Report  more

TitleSimilaritySource
None found