Techbio specialist Relation Therapeutics Ltd has raised $35 million in new seed funding, bringing total seed money to $60 million, as it advances development of its in silico/wet lab platform for identifying drug targets in the non-coding parts of the genome. The company is building a “lab in the loop” system where in depth ‘omics profiles of single cells from fresh patient tissues are analyzed by its machine learning engine to uncover the genetic basis of clinical phenotypes and identify novel targets.
Researchers from the University of Edinburgh seek protection for an algorithm developed using artificial intelligence that could be used by doctors to diagnose heart attacks more quickly and effectively.
Biocom California’s Global Life Science Partnering & Investor Conference kicked off with a panel discussion focused on artificial intelligence (AI) in drug discovery. While there’s been a lot of hype over how AI and machine learning have the potential to help companies speed up drug development, panelists hypothesized the largest opportunities are in developing new capabilities, potentially increasing the success rate going from discovery to regulatory approval.
The U.S. FDA’s device center has at times struggled to make the volume of hires under the reigning Medical Device User Fee Agreement (MDUFA), but that wasn’t a problem in fiscal year 2023.
Researchers have reported that the predictive abilities of a machine learning algorithm trained using best practices on a large clinical dataset did not generalize beyond the data that was used to train it. The algorithm was able to predict, to a degree, which individual patients would benefit from the medication when the patients were from the dataset the algorithm was trained on. But when it was supposed to predict who would benefit in clinical cohorts that were not part of the training, it performed no better than chance.
Researchers have reported that the predictive abilities of a machine learning algorithm trained using best practices on a large clinical dataset did not generalize beyond the data that was used to train it.
The first patenting from Mhealthcare Inc. describes a patient examination table or bed equipped with a variety of sensors, data from which may be analyzed with trained machine learning models to facilitate risk assessment and diagnosis of non-neurotypical developmental conditions such as autism in infants and young children by predicting cognitive, behavioral, social and developmental outcomes as early as the first three months of life. It is also claimed that the table may be used to diagnose epilepsy and Alzheimer’s disease.
Eisai Co. Ltd. and Oita University in Oita Prefecture, Japan, developed a first-of-its-kind machine learning model to predict amyloid beta accumulation in the brain using a wristband sensor. The model, which collects biological and lifestyle data from daily life, is expected to enable screening for brain amyloid beta accumulation to identify those at risk for Alzheimer's disease, particularly because amyloid beta begins to accumulate in the brain about 20 years before the onset of the disease.
The U.S. FDA might still be seen as the premier med tech regulatory entity in the world, but the agency is badly outnumbered by companies in the life sciences, which are pumping out artificial intelligence algorithms at a breathtaking pace. Further, the FDA must also avoid being lapped by industry in connection with the regulatory novelty known as the predetermined change control plan, a challenge that put the agency’s device center in scramble mode for essentially the entirety of calendar year 2023.
Eisai Co. Ltd. and Oita University in Oita Prefecture, Japan, developed a first-of-its-kind machine learning model to predict amyloid beta accumulation in the brain using a wristband sensor. The model, which collects biological and lifestyle data from daily life, is expected to enable screening for brain amyloid beta accumulation to identify those at risk for Alzheimer's disease, particularly because amyloid beta begins to accumulate in the brain about 20 years before the onset of the disease.