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Machine learning uncovers potential new TB drugs

Posted
October 15, 2020

Computational method for screening drug compounds can help predict which ones will work best against tuberculosis or other diseases.

Machine learning is a computational tool used by many biologists to analyze huge amounts of data, helping them to identify potential new drugs. MIT researchers have now incorporated a new feature into these types of machine-learning algorithms, improving their prediction-making ability.

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News

New gene regulation model provides insight into brain development

Posted
September 8, 2020

 

A well-known protein family binds to many more RNA sequences than previously thought in order to help neurons grow.

In every cell, RNA-binding proteins (RBPs) help tune gene expression and control biological processes by binding to RNA sequences. Researchers often assume that individual RBPs latch tightly to just one RNA sequence. For instance, an essential family of RBPs, the Rbfox family, was thought to bind one...

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Bringing RNA into genomics

Posted
July 29, 2020

ENCODE consortium identifies RNA sequences that are involved in regulating gene expression.

 

The human genome contains about 20,000 protein-coding genes, but the coding parts of our genes account for only about 2 percent of the entire genome. For the past two decades, scientists have been trying to find out what the other 98 percent is doing.

 

A research consortium known as ENCODE (Encyclopedia of DNA Elements) has made significant...

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3 Questions: Ibrahim Cissé on using physics to decipher biology

Posted
July 22, 2020

                                                     

A biophysicist employs super-resolution microscopy to peer inside living cells and witness never-before-seen phenomena

How do cells use physics to carry out biological processes? Biophysicist Ibrahim Cissé explores this fundamental question in his interdisciplinary laboratory, leveraging super-resolution microscopy to probe the properties of...