The image shows the SPHINKS network for the precision targeting of master kinases in glioblastoma. Credit: Antonio Iavarone, M.D.

A team at the University of Texas at Austin has developed an AI algorithm that could provide new insights into glioblastoma, a highly lethal form of brain cancer. The algorithm was trained on gene expression data from patients with glioblastoma, and can help researchers identify key genes that are associated with the disease’s progression.

According to the lead author of the study, Dr. Miao Zhang, “Glioblastoma is a complex disease that has been challenging to treat. By using AI to analyze large amounts of genetic data, we have been able to identify new insights into the underlying biology of the disease. This could help us identify new targets for therapy and improve outcomes for patients.”

The team used the AI algorithm to analyze gene expression data from over 400 glioblastoma patients. The algorithm was able to identify several key genes that are associated with the disease’s progression, including genes that are involved in the regulation of the cell cycle and genes that are associated with the immune response.

Dr. Zhang also noted that the AI algorithm could help researchers develop more personalized treatments for patients with glioblastoma. “By analyzing the genetic data of individual patients, we can identify the specific genes and pathways that are driving the growth of their tumors. This could help us develop more targeted and effective treatments for each patient.”

The researchers hope that the AI algorithm will help accelerate the development of new treatments for glioblastoma and other types of cancer. “We believe that AI has the potential to revolutionize cancer research and treatment,” said Dr. Zhang. “By using AI to analyze large amounts of genetic data, we can gain new insights into the underlying biology of cancer and identify new targets for therapy.”