The importance of artificial intelligence and machine learning continues to be acknowledged by drug development companies. Recently, to help accelerate the discovery of therapies to treat COVID-19, several deals have been forged to deploy those tools.
When Zebiai Therapeutics Inc.’s CEO, Rick Wagner, went about naming his new machine learning company, he wanted it to connote something dramatic that displayed the company’s potential to reach into the seemingly boundless future technology had unlocked.
Machine learning and artificial intelligence (AI) are already being actively used in drug discovery to evaluate potential binding of small-molecule drugs to proteins, but there's potential for the technologies to be used on the development side as well, especially in hard-to-treat diseases such as Alzheimer's disease.
Startup firm Dyno Therapeutics Inc. is attempting to engineer a new generation of adeno-associated virus (AAV) capsids by navigating its way across what it calls the “capsid fitness landscape,” in order to optimize the key parameters that affect capsid performance: production, delivery efficiency, biodistribution, immunogenicity and thermostability.
SAN JOSE, Costa Rica – Cross reality (XR) technology is gaining traction in the med-tech sector thanks to advancements in the virtual reality (VR), augmented reality (AR) and mixed reality (MR) fields that comprise it, triggered by a surge of investments that have driven cash flow to med-tech startups. The new technology is already impacting the health care sector.
BOSTON – For diagnosing nonalcoholic steatohepatitis (NASH), liver biopsy is "the reference standard," Dean Hum, president of Genfit Corp., told BioWorld MedTech. "I'm not going to call it the gold standard."
The emerging methodology of federated learning can overcome many of the ethical and privacy obstacles preventing patient data from being pooled for analysis, according to research published this week.
The emerging methodology of federated learning can overcome many of the ethical and privacy obstacles preventing patient data from being pooled for analysis, according to research published this week. French-American artificial intelligence (AI) specialist Owkin Inc. has demonstrated the technique can be used to apply machine learning to datasets held at different clinical centers. In a paper published in the Oct. 7 online edition of Nature Medicine, they describe how that generated new insights into the histopathology of malignant mesothelioma.
Researchers from Johns Hopkins University School of Medicine have developed a machine learning program that could score the risk of pancreatic cysts and recommend one of three treatment strategies – surgery, watchful waiting or discharge without follow-up – more accurately than current methods. The program could potentially reduce the number of unnecessary surgeries performed on pancreatic cysts with little to no potential of turning cancerous.
