Combining the biological data generated from Body-on-a-Chip with the computational power of machine learning and Artificial Intelligence should …

video: Researchers used artificial intelligence to analyze thousands of videos from a database of American Sign Language—this clip shows the sign …

Artificial intelligence and robotics uncover hidden signatures of Parkinson's disease.

"By using machine learning to process the data, we felt we could revisit the canonical view of a cell," Weigel says.

A University of Glasgow study developed machine learning models that could potentially identify animal viruses capable of infecting humans and classify how much of a risk they pose to humans by analyzing the genomes of viruses. The model outperformed models based on phylogenetic relatedness of specific viruses to other viruses known to infect humans according to the study, published in the peer-reviewed PLOS Biology. The model could have predicted SARS-CoV-2 as a high-risk coronavirus strain, said researchers. Analyses of the model show that there could be generalizable features of viral genomes that may make viruses preadapt to infect humans. These features are independent of the virus taxonomic relationships.

Deep Longevity scientists reveal a comprehensive integrated system of aging and longevity biomarkers dubbed "deep aging clocks" to be released on 29th of September, 2020 Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

Medical researchers are employing AI to search through databases of known drugs to see if any can be associated with a treatment for the new COVID-19 coronavirus. An early success story comes from BenevolentAI of London, which using tools developed to search through medical literature, identified rheumatoid arthritis drug baricitinib as a possible treatment for COVID-19. In a pilot study at the end of March, 12 adults with moderate COVID-19 admitted to the hospital in either Alessandria or Prato, Italy, received a daily dose of baricitinib, along with an anti-HIV drug combination of lopinavir and ritonavir, for two weeks. Another study group of 12 received just lopinavir and ritonavir. After their two-week treatment, the patients who received baricitinib had mostly recovered, according to a recent account in The Scientist.

ALBUQUERQUE, N.M. -- A proposed project to help scientists use the laws of physics to view multiscale physical events with a clarity never before achieved has won an Early Career Research Program award from the Department of Energy for Sandia National Laboratories researcher Nathaniel Trask. Such work may require observations over a millionfold change in scale, with features ranging from the meter- to microscale. Sandia National Laboratories researcher Nat Trask, winner of the Department of Energy's Early Career award, is researching how to clearly present huge changes in scale. Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

Allen, associate professor of statistics, computer science and electrical and computer engineering at Rice and of pediatrics-neurology at Baylor College of Medicine, will address the topic in both a press briefing and a general session today at the 2019 Annual Meeting of the American Association for the Advancement of Science (AAAS). "The question is, 'Can we really trust the discoveries that are currently being made using machine-learning techniques applied to large data sets?'" "The answer in many situations is probably, 'Not without checking,' but work is underway on next-generation machine-learning systems that will assess the uncertainty and reproducibility of their predictions." Machine learning (ML) is a branch of statistics and computer science concerned with building computational systems that learn from data rather than following explicit instructions. Allen said much attention in the ML field has focused on developing predictive models that allow ML to make predictions about future data based on its understanding of data it has studied. "A lot of these techniques are designed to always make a prediction," she said.

Virginia Tech researchers are using brain-inspired machine learning techniques to increase the energy efficiency of wireless receivers. Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.