Using artificial intelligence (AI) technology called deep learning, MIT researchers have identified compounds capable of defeating methicillin-resistant Staphylococcus aureus (MRSA), a drug-resistant bacterium causing over 10,000 deaths annually in the US. The compounds, which exhibit low toxicity to human cells, were found to effectively kill MRSA in lab and mouse models, making them potential drug…
Using artificial intelligence in the form of deep learning, researchers from MIT have discovered compounds capable of killing methicillin-resistant Staphylococcus aureus (MRSA), a drug-resistant bacteria that reportedly causes over 10,000 deaths in the U.S. each year. This breakthrough was achieved by training a deep learning model using predictive information based on antibiotic potency of a…
A powerful new class of antibiotics capable of killing drug-resistant bacteria has been discovered by researchers at the Massachusetts Institute of Technology (MIT), by utilizing a subtype of artificial intelligence (AI) known as deep learning. Results from the study, published in the journal Nature, demonstrate the compound's effectiveness against Methicillin-Resistant Staphylococcus Aureus (MRSA), a bacterium…
In the field of biomedicine, segmentation refers to the process of highlighting important structures in a medical image, from organs to cells. Artificial intelligence (AI) models are starting to play a pivotal role in this task, but there are limitations with most existing models, mainly due to the fact that they are unable to factor…
A team at MIT, along with the Broad Institute of MIT and Harvard, and Massachusetts General Hospital, has developed an artificial intelligence (AI) tool that can help navigate the uncertainty in medical image analysis. The tool, named Tyche, provides multiple possible interpretations of a medical image rather than the single answer typically provided by AI…
Scientists from the McGovern Institute for Brain Research at MIT, the Broad Institute of MIT and Harvard, and the National Center for Biotechnology Information at the National Institutes of Health, have developed a new search algorithm to find enzymes of interest in vast microbial sequence databases. This algorithm, called Fast Locality-Sensitive Hashing-based clustering (FLSHclust), discovered…
Researchers at MIT, Harvard, and the National Institutes of Health have utilized a new search algorithm to identify 188 different types of rare CRISPR systems in bacterial genomes. This data holds potential to advance genome-editing technology, enabling more precise treatments and diagnostics.
The algorithm, developed in the lab of prominent CRISPR researcher, Professor Feng Zhang uses…
Scientists from the McGovern Institute for Brain Research at MIT, the Broad Institute of MIT and Harvard, and the National Center for Biotechnology Information at the National Institutes of Health have developed a new algorithm that can sift through massive amounts of genomic data to identify unique CRISPR systems. Known as Fast Locality-Sensitive Hashing-based clustering…