Bayesian learning boosts gene research accuracy microbiologystudy

Researchers at The University of Texas at Arlington have developed a new computational tool that helps scientists pinpoint proteins known as transcriptional regulators that control how genes turn on and off.

“These proteins are central to many biological processes, including growth, development and disease,” said senior author Xinlei (Sherry) Wang, a Jenkins Garrett professor of statistics and data science.

In a study recently published in Nature Communications, Dr. Wang and her colleagues—Zeyu Lu, a postdoctoral researcher in her lab at UTA, and Lin Xu, a researcher at UT Southwestern Medical School—introduce a tool called Bayesian Identification of Transcriptional Regulators from Epigenomics-Based Query Regions Sets, or BIT.

BIT framework uses Bayesian hierarchical modeling, which assesses probabilities across multiple layers of evidence rather than evaluating isolated pieces of information. This approach enables scientists to more confidently identify transcriptional regulators, or TRs, even in complex biological environments where multiple TRs may be active at once.

Rather than analyzing individual TRs in isolation, BIT integrates vast amounts of data to give scientists a clearer picture of which regulators are active and how they function. This makes BIT a powerful tool for studying transcriptional regulation.

When TRs malfunction, they can contribute to a wide range of health issues, including cancer, Wang said.

“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,” she said. “Our research offers a more advanced approach, using a vast library of epigenomics data to identify these proteins with greater accuracy and interpretability.”

One of BIT’s most promising applications is in cancer research. By identifying TRs essential for tumor survival, scientists can uncover potential weak points in cancer cells. This knowledge could lead to new treatment strategies that target specific TRs to halt tumor growth.

“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 can also help investigations into metabolic disorders, heart disease and other conditions where transcriptional regulation plays a crucial role. Since TRs influence a wide range of biological functions, gaining a deeper understanding of them could drive breakthroughs across many areas of medicine.

“The development of BIT highlights how powerful machine learning and advanced statistical methods have become in modern biomedical research,” said Dr. Lu, whose dissertation advisor was Wang, with Dr. Xu serving as co-advisor. “As more scientists turn to computational tools to analyze complex genetic and epigenomic data, models like BIT will likely become essential for uncovering new biological insights.”

By improving the ability to identify critical regulators with confidence, Lu said, BIT helps researchers bridge the gap between raw epigenomic data and meaningful discoveries. This could speed up scientific breakthroughs in disease research, drug development and even personalized medicine.