Effective protein engineering can give us control over the generated products inside a cell. However, for many of the biochemical reactions responsible for these products, we don't we don't know the specific protein- or enzyme-producing gene responsible. These reactions are called "orphan" and have become a big problem for protein engineers.

As artificial intelligence continues to evolve, diagnosing disease faster and potentially with greater accuracy than physicians, some have suggested that technology may soon replace tasks that physicians currently perform. But a new study from the Google AI research group shows that physicians and algorithms working together are more effective than either alone.

As the threat of antibiotic resistance looms, microbiologists aren’t the only ones thinking up new solutions. James Zou, PhD, assistant professor of biomedical data science at Stanford, has applied machine learning to create an algorithm that generates thousands of entirely new virtual DNA sequences with the intent of one day creating antimicrobial proteins.

Researchers from the Stanford University School of Medicine presented preliminary results of the Apple Heart Study, an unprecedented virtual study with over 400,000 enrolled participants. The researchers reported that wearable technology can safely identify heart rate irregularities that subsequent testing confirmed to be atrial fibrillation, a leading cause of stroke and hospitalization in the United States.

A first-of-its-kind nanoparticle vaccine candidate for respiratory syncytial virus (RSV) has been designed in an international research effort. RSV is second only to malaria as a cause of infant mortality worldwide. The new vaccine elicits potent neutralizing antibodies against RSV in both mice and monkeys. The animal research findings, reported March 7 in the journal Cell, pave the way for human clinical trials.

Breast cancer is the most common cancer in women, and despite important improvements in therapy, it is still a major cause for cancer-related mortality, accounting for approximately 500,000 annual deaths worldwide. Breast cancer screening programs using mammography are effective in reducing breast cancer-related mortality.

Machine learning has improved dramatically in recent years and shown great promise in the field of medical image analysis. A team of research specialists at Dartmouth's Norris Cotton Cancer Center have utilized machine learning capabilities to assist with the challenging task of grading tumor patterns and subtypes of lung adenocarcinoma, the most common form of the leading cause of cancer-related deaths worldwide.