GALVESTON, Texas – Complex diseases involve many different types of human data like genes, proteins, social interactions and the environment, just to name a few. Biomedical researchers all over the world are struggling to integrate these varied types of human data to develop treatments for complex diseases like Alzheimer's and diabetes.

One of the stumbling blocks is that current analytic tools are not designed so that team members with different backgrounds (such as a physician, molecular biologist and statistician), are able to combine their expertise to analyze many different types of data simultaneously. Current tools mostly are designed to analyze just a few types of data such as genes or proteins in isolation by individual researchers.

Dr. Suresh Bhavnani, professor of biomedical informatics at the Institute for Translational Sciences at the University of Texas Medical Branch at Galveston, says what is needed is “team-centered informatics” tools that help multidisciplinary teams get a holistic understanding of complex diseases, hidden in large and varied data sets.

That is what he describes in his new paper published in the Journal of Applied Behavioral Science, which received an outstanding paper award at the Science of Team Science conference this year.

In this paper, Bhavnani proposes that for team-centered informatics to be successful, many different types of information involved in complex diseases need to be presented and analyzed in such a way that while each member on the team can understand the parts related to their expertise, the team as a whole can understand how all the different parts interact, allowing them to have insights that cross disciplinary boundaries.

“These tools could be designed as computational boundary objects that on the one hand are meaningful to individual experts specializing in the analysis of a single data type such as genes, but on the other hand also provide an integrated understanding of all data types leading to insights that transcend individual disciplines” Bhavnani said. “Team members working together could filter, layer, and intersect different data types through multiple contrasting views that impact each other’s understanding of the disease.”

The paper describes how this approach was used by a team consisting of a physician, molecular biologist, statistician, and informatician to analyze a dataset of severe asthma patients. The approach led the team members to rapidly integrate knowledge from each of their disciplines to design a new precision-medicine approach for treating severe asthma.

“The team-centered informatics approach we used was critical in enabling this new insight,” Bhavnani said.

To learn more about Bhavnani’s team-centered informatics approach, watch the video on the following website:


This study 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.