Please see the most updated version here . Was there a specific task in mind? Was there a specific gap that needed to be filled? The MOTI VE dataset was created to promote the development of new drug-target interaction (DTI) prediction models based on both, existing relationships between compounds and their protein targets, and the similarity of JUMP Cell Painting morphological features of perturbed cells [2].The MOTI VE dataset was created with the DTI task in mind, and addresses a lack of graph-based biological datasets with empirical node features. Who created this dataset (e.g., which team, research group) and on behalf of which entity (e.g., company, institution, organization)? This dataset was created by the Carpenter-Singh Lab in the Imaging Platform at the Broad Institute of MIT and Harvard, Cambridge, Massachusetts. What support was needed to make this dataset? If there is an associated grant, provide the name of the grantor and the grant name and number, or if it was supported by a company or government agency, give those details.) The authors gratefully acknowledge an internship from the Massachusetts Life Sciences Center (to ES).

A capsule that could replace insulin shots. In Giovanni Traverso's lab, the focus is always on making life better for patients. Every Monday, more than a hundred members of Giovanni Traverso's Laboratory for Translational Engineering (L4TE) fill a large classroom at Brigham and Women's Hospital for their weekly lab meeting. With a social hour, food for everyone, and updates across disciplines from mechanical engineering to veterinary science, it's a place where a stem cell biologist might weigh in on a mechanical design, or an electrical engineer might spot a flaw in a drug delivery mechanism. And it's a place where everyone is united by the same goal: engineering new ways to deliver medicines and monitor the body to improve patient care. Traverso's weekly meetings bring together a mix of expertise that lab members say is unusual even in the most collaborative research spaces. But his lab--which includes its own veterinarian and a dedicated in vivo team--isn't built like most.

Artificial intelligence has helped discover a new class of antibiotics that can treat infections caused by drug-resistant bacteria. This could help in the battle against antibiotic resistance, which was responsible for killing more than 1.2 million people in 2019 – a number expected to rise in the coming decades. Testing in mice showed that the new antibiotic compounds proved promising treatments for both Methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant Enterococcus – a bacterium that has developed resistance to the drug typically used for treating MRSA infections. "Our [AI] models tell us not only which compounds have selective antibiotic activity, but also why, in terms of their chemical structure," says Felix Wong at the Broad Institute of MIT and Harvard in Massachusetts. Wong and his colleagues set out to show that AI-guided drug discovery could go beyond identifying specific targets that drug molecules can bind to, and instead predict the biological effect of entire classes of drug-like compounds.

Advancements in whole genome sequencing have ignited a revolution in digital biology. Genomics programs across the world are gaining momentum as the cost of high-throughput, next-generation sequencing has declined. Whether used for sequencing critical-care patients with rare diseases or in population-scale genetics research, whole genome sequencing is becoming a fundamental step in clinical workflows and drug discovery. But genome sequencing is just the first step. Analyzing genome sequencing data requires accelerated compute, data science and AI to read and understand the genome.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Nvidia has been a leader in providing AI and digital twin infrastructure for the medical community. Its various offerings improve diagnostics, the development of new medical devices, medical research and drug development. At the Fall GTC Conference, Nvidia announced various new medical tools, partnerships and workflows.

Atrial fibrillation--an irregular and often rapid heart rate--is a common condition that often leads to the formation of clots in the heart that can travel to the brain to cause a stroke. As described in a study published in Circulation, a team led by researchers at Massachusetts General Hospital (MGH) and the Broad Institute of MIT and Harvard has developed an artificial intelligence–based method for identifying patients who are at risk for developing atrial fibrillation and could therefore benefit from preventative measures. The investigators developed the artificial intelligence–based method to predict the risk of atrial fibrillation within the next five years based on results from electrocardiograms (noninvasive tests that record the electrical signals of the heart) in 45,770 patients receiving primary care at MGH. Next, the scientists applied their method to three large data sets from studies including a total of 83,162 individuals. The AI-based method predicted atrial fibrillation risk on its own and was synergistic when combined with known clinical risk factors for predicting atrial fibrillation. The method was also highly predictive in subsets of individuals such as those with prior heart failure or stroke.

Atrial fibrillation -- an irregular and often rapid heart rate -- is a common condition that often leads to the formation of clots in the heart that can travel to the brain to cause a stroke. The study was published in Circulation. The investigators developed the artificial intelligence-based method to predict the risk of atrial fibrillation within the next five years based on results from electrocardiograms (noninvasive tests that record the electrical signals of the heart) in 45,770 patients receiving primary care at MGH. Next, the scientists applied their method to three large data sets from studies including a total of 83,162 individuals. The AI-based method predicted atrial fibrillation risk on its own and was synergistic when combined with known clinical risk factors for predicting atrial fibrillation. The method was also highly predictive in subsets of individuals such as those with prior heart failure or stroke.

BOSTON – Atrial fibrillation--an irregular and often rapid heart rate--is a common condition that often leads to the formation of clots in the heart that can travel to the brain to cause a stroke. As described in a study published in Circulation, a team led by researchers at Massachusetts General Hospital (MGH) and the Broad Institute of MIT and Harvard has developed an artificial intelligence–based method for identifying patients who are at risk for developing atrial fibrillation and could therefore benefit from preventative measures. The investigators developed the artificial intelligence–based method to predict the risk of atrial fibrillation within the next five years based on results from electrocardiograms (noninvasive tests that record the electrical signals of the heart) in 45,770 patients receiving primary care at MGH. Next, the scientists applied their method to three large data sets from studies including a total of 83,162 individuals. The AI-based method predicted atrial fibrillation risk on its own and was synergistic when combined with known clinical risk factors for predicting atrial fibrillation. The method was also highly predictive in subsets of individuals such as those with prior heart failure or stroke.

Using a machine-learning algorithm, MIT researchers have identified a powerful new antibiotic compound. In laboratory tests, the drug killed many of the world's most problematic disease-causing bacteria, including some strains that are resistant to all known antibiotics. It also cleared infections in two different mouse models. The computer model, which can screen more than a hundred million chemical compounds in a matter of days, is designed to pick out potential antibiotics that kill bacteria using different mechanisms than those of existing drugs. "We wanted to develop a platform that would allow us to harness the power of artificial intelligence to usher in a new age of antibiotic drug discovery," says James Collins, the Termeer Professor of Medical Engineering and Science in MIT's Institute for Medical Engineering and Science (IMES) and Department of Biological Engineering.