Immune system researchers have designed a computational tool to improve pandemic preparedness. Scientists can use this new algorithm to compare data from very different experiments and more accurately predict how individuals will respond to disease.
“While we are trying to understand how individuals fight off different viruses, the advantage of our method is that it can be applied to other organisms, such as comparing different drugs or different cancer cell lines. It has general applicability in academic settings,” says Dr. Tal Einab. D., La Jolla Institute of Immunology (LJI) assistant professor and co-leader of the new study.
This study addresses a major challenge in medical research. Labs that study infectious diseases collect very different types of data, even those that focus on the same virus. “Each dataset becomes its own independent island,” he says Einav.
Working closely with Dr. Rong Ma, a postdoctoral fellow at Stanford University, Einav set out to develop an algorithm to help compare large datasets. His inspiration comes from a background in physics, where scientists can be confident that their data falls within the known laws of physics, no matter how innovative the experiment. E is always equal to mc2.
For example, researchers may be able to design better vaccines by understanding exactly how human antibodies target viral proteins.
The new method is also thorough enough to give scientists confidence behind their predictions. In statistics, a “confidence interval” is a way to quantify how certain a scientist’s predictions are.
“When people from different backgrounds come together, there is great synergy,” says Einab. “With the right team, we can finally solve these big unsolved problems.”
Source: scitechdaily.com
