A team from UT Arlington and UT Southwestern Medical School has developed a new computational framework that could help scientists identify key proteins that drive diseases like cancer. By improving the way researchers pinpoint these gene regulators, the tool could lay groundwork for more precise treatments in the future.

UTA announced that the new approach, called BIT (short for Bayesian Identification of Transcriptional Regulators), uses advanced statistics and Bayesian learning to analyze vast epigenomic datasets.

This framework helps scientists identify transcriptional regulators — proteins that control how genes switch on and off — with more accuracy than traditional methods, according to UTA.

“These proteins are central to many biological processes, including growth, development and disease,” said Xinlei (Sherry) Wang, a Jenkins Garrett professor of statistics and data science at UTA and senior author of the study, which was recently published in Nature Communications.

Wang developed the tool, with UTA postdoctoral researcher Zeyu Lu and Lin Xu, a researcher at UT Southwestern Medical School.

The BIT framework

Lin Xu, a researcher at UT Southwestern Medical School, collaborated on the BIT tool to help scientists better identify the gene regulators that can drive diseases like cancer. [Photo via UTA]

BIT uses Bayesian hierarchical modeling to assess probabilities across multiple layers of evidence, rather than analyzing data points in isolation. This allows scientists to more accurately identify which transcriptional regulators (TRs) are active, even in complex biological environments.

“Researchers like me have long struggled to identify accurately which TRs are active in specific biological settings because traditional methods rely on markers like their binding motifs on DNA, which can be imprecise,” Wang said.

She says the team’s research offers a more advanced approach, using a large library of epigenomics data to accurately identify proteins and provide greater interpretability.

Rather than evaluate TRs one by one, BIT integrates vast datasets to give researchers a clearer picture of how multiple regulators function simultaneously. This is particularly valuable for disease research, where identifying the wrong protein target can lead to ineffective or harmful treatments.

Targeting cancer and other diseases

UTA said one of BIT’s most promising applications is in cancer research. By identifying TRs essential for tumor survival, scientists could uncover weak points in cancer cells and develop therapies to shut down those specific regulators.

“This advancement is significant because TRs influence many aspects of human health, and determining which ones are active can provide deeper insight into diseases and potential treatments,” Wang said. “For example, in cancer, dysregulated TRs can cause uncontrolled cell growth, leading to tumors. Knowing which TRs are involved in this process can help researchers develop targeted treatments that block harmful TR activity while preserving normal cellular functions.”

Beyond cancer, BIT could also support research into metabolic disorders, cardiovascular disease, and other conditions in which transcriptional regulation plays a role.

‘Powerful machine learning’ in biomedical discovery