A team of LSU researchers has developed a way to determine which drug therapies work best against an individual's unique type of cancer, possibly providing a way to find cures more quickly and make treatment more affordable. The interdisciplinary team includes researchers from the School of Veterinary Medicine, College of Science, College of Engineering and the Center for Computation & Technology. It created CancerOmicsNet, a new drug discovery engine run by artificial intelligence. Using algorithms originally designed to map complex social networks, like those utilized by Facebook, researchers generated three-dimensional graphs of molecular datasets that include cancer cell lines, drug compounds and interactions among proteins inside the human body. The graphs are then analyzed and interconnected by AI, forming a much clearer picture of how a specific cancer would respond to a specific drug.

Researchers have developed a new algorithm for clinical use. It is based on artificial intelligence and compares the cells of sick individuals with a reference atlas of healthy cells. In practice, doctors can use it to accurately identify diseased cells. This is a major advantage for personalized medicine. The Human Cell Atlas is the world's largest, continuously growing single-cell reference atlas.

The human immune defense is based on the ability of white blood cells to accurately identify disease-causing pathogens and to initiate a defense reaction against them. The immune defense is able to recall the pathogens it has encountered previously, on which, for example, the effectiveness of vaccines is based. Thus, the immune defense the most accurate patient record system that carries a history of all pathogens an individual has faced. This information however has previously been difficult to obtain from patient samples. The learning immune system can be roughly divided into two parts, of which B cells are responsible for producing antibodies against pathogens, while T cells are responsible for destroying their targets.

The human immune defense is based on the ability of white blood cells to accurately identify disease-causing pathogens and to initiate a defense reaction against them. The immune defense is able to recall the pathogens it has encountered previously, on which, for example, the effectiveness of vaccines is based. Thus, the immune defense the most accurate patient record system that carries a history of all pathogens an individual has faced. This information however has previously been difficult to obtain from patient samples. The learning immune system can be roughly divided into two parts, of which B cells are responsible for producing antibodies against pathogens, while T cells are responsible for destroying their targets.

A new artificial intelligence (AI) system trained on artificial x-rays is helping researchers identify rare medical conditions in medical images. Researchers from the University of Toronto have developed a new AI system that enables machine learning to create computer generated X-rays that augment AI training sets, which could improve the speed and accuracy of medical diagnostics. "In a sense, we are using machine learning to do machine learning," Shahrokh Valaee, a professor in the Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE) at the University of Toronto, said in a statement. "We are creating simulated X-rays that reflect certain rare conditions so that we can combine them with real X-rays to have a sufficiently large database to train the neural networks to identify these conditions in other X-rays.

"Now I like artificial intelligence," said Karla Savage, a 71-year-old bioblitz participant from the East Bay. "I guess the only application I saw before was people losing jobs (to artificial intelligence), but to have this recognition software be used in a different way than just jobs and economy -- I just wasn't …