This September, MIT Hacking Medicine is hosting the BioxAI Pitch Event. The event will be an opportunity to bring together budding entrepreneurs from various MIT departments, namely PhD students and postdocs, applying ML/AI to biological questions, with a focus on protein biology/drug discovery. For example, early stage founders will pitch for co-founders (max. 2min). Founders and individuals who want to join a team will likewise pitch themselves. This will be an opportunity to learn from guests within and outside MIT, including NSF, CSAIL Alliances, and the Martin Trust Center.
Are you a CSAIL entrepreneur? Are you curious about the resources that CSAIL Alliances, as well as the rest of the MIT Ecosystem can offer you? Sign up for Office Hours using the form to ask Christiana Kalfas, Sr.
On a research cruise around Hawaii in 2018, Yuening Zhang SM ’19, PhD ’24 saw how difficult it was to keep a tight ship. The careful coordination required to map underwater terrain could sometimes led to a stressful environment for team members, who might have different understandings of which tasks must be completed in spontaneously changing conditions. During these trips, Zhang considered how a robotic companion could have helped her and her crewmates achieve their goals more efficiently.
An MIT study published today in Nature provides new evidence for how specific cells and circuits become vulnerable in Alzheimer’s disease, and hones in on other factors that may help some people show resilience to cognitive decline, even amid clear signs of disease pathology. To highlight potential targets for interventions to sustain cognition and memory, the authors engaged in a novel comparison of gene expression across multiple brain regions in people with or without Alzheimer’s disease, and conducted lab experiments to test and validate their major findings.
Because machine-learning models can give false predictions, researchers often equip them with the ability to tell a user how confident they are about a certain decision. This is especially important in high-stake settings, such as when models are used to help identify disease in medical images or filter job applications.
Artificial intelligence models often play a role in medical diagnoses, especially when it comes to analyzing images such as X-rays. However, studies have found that these models don’t always perform well across all demographic groups, usually faring worse on women and people of color.
The internet is awash in instructional videos that can teach curious viewers everything from cooking the perfect pancake to performing a life-saving Heimlich maneuver.
When you’re trying to understand which diseases or physical traits you’re predisposed to, the answers are sprinkled across your DNA. One powerful method for decoding this genetic forecast is polygenic scores, which give patients estimates of their risk for a condition and the likelihood of having physical characteristics (phenotypes, like being tall). Researchers seek to improve the accuracy of these cumulative predictions to account for most of the known genetic contributions.
Your heart is a tireless organ that beats about 3 billion times over an average lifetime and is simply essential for life. Unsurprisingly, cardiovascular disease is the leading cause of death worldwide, costing millions of lives each year. This relentless condition primarily damages the heart, which is divided into four main chambers: the right atrium, left atrium, right ventricle, and left ventricle. Understanding the functions and vulnerabilities of these chambers is crucial in the fight against heart disease.
