Does the cure for different types of cancer exist in 50 disparate labs? What about effective treatment for Alzheimer’s disease? According to Suresh Bhavnani, Ph.D., professor of biomedical informatics at the Institute for Translational Science at the University of Texas Medical Branch at Galveston (UTMB), answers to some of medicine’s biggest challenges might already exist.
The key, he believes, is to enable multidisciplinary experts to integrate different aspects of diseases in large datasets in just the right way—and Bhavnani, who began his career in architectural design in India, then fell in love with computer science when he immigrated to the United States, has created a blueprint for doing just that.
He described his proposal—what he is calling a “team-centered informatics” approach—in a new paper published in the Journal of Applied Behavioral Science, which was awarded an outstanding paper award at the Science of Team Science conference earlier this year. According to Bhavnani, team-centered informatics are designed to help multidisciplinary experts gain an integrated understanding of complex conditions and diseases from large datasets that are typically diverse and contain different aspects of human disease.
“There’s big data everywhere, but there is this whole revolution of multi-omics data, which is not just big data but it’s different types of data. You have genetic information, you have proteomic information, you have social, environmental—and we believe that a lot of diseases involve all of them together,” Bhavnani said. “That’s where the problem begins, because if such multi-omics data is going to be made available, you don’t have a single person who can understand and analyze all of it.”
Yet this, Bhavnani said, is critical for driving and accelerating new discoveries.
“The notion of team-centered informatics is, essentially: How do you put all of this information together in a single representation that everyone can understand?” he explained. “What I learned from architectural design is that the visual language is something that crosses boundaries in a very powerful way, but you have to do it right. And then, you have to bring the team together, and the team has to interact with this computational boundary object that spans these different disciplines. That’s when discoveries can happen.”
The technology would allow, for instance, a genomics researcher, an immunologist, a statistician, and a nurse to use their expertise in different aspects of a disease—be it molecular, clinical, or environmental—into a single visual representation wherein each would be able to easily access and comprehend the complex relationships between the variables. Ideally, Bhavnani said, the technology would transcend disciplinary barriers, such as technical terms and research cultures.
The design of team-centered informatics, he explained, is four-fold. First, it requires representations of data that are comprehensible across the disciplines in the team. Second, it must enable quantitative analysis of associations among diverse types of variables. Third, it would need to be interactive so that team members can together explore the data and watch for patterns, and, finally, the research would need to be translatable to other scientists, physicians and patients.
Already, Bhavnani’s lab has had success working with a group focused on patients with severe asthma; a physician, molecular biologist, statistician and informatician created a novel approach for treatment after integrating different aspects of the disease to identify complex patterns. He hopes that others will read his paper and find inspiration to embrace a future of multidisciplinary collaboration for analyzing patterns in large and diverse datasets through creating similar technology.
“It’s a vision,” he said. “I’m showing you a glimpse of that vision in the paper.”
Perhaps that will be all it takes to make the next great discovery in medicine.
Visit teamcenteredinformatics.com for more information, including a video about the project.
Update: The study cited in this article was conducted with the support of the Institute for Translational Sciences at the University of Texas Medical Branch, supported in part by a Clinical and Translational Science Award (UL1 TR001439) from the National Center for Advancing Translational Sciences, National Institutes of Health, and in part by a Patient-Centered Outcomes Research Institute contract (ME-1511-33194).
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