The study examined 9386 driver mutations and 8728 passenger mutations in glioblastoma (file photo)
GBMDriver, a freely accessible web server, was developed to identify mutations in glioblastoma, a tumor in the brain and spinal cord, and other types of cancer.
Indian Institute Researcher technology Madras has recently developed ‘GBMDriver’ (GlioBlastoma Mutiforme Drivers), a machine learning-based computational tool for the identification of cancer-causing tumors in the brain and spinal cord.
GBMDriver, a freely accessible web server, is designed to detect driver mutations and passenger mutations (neutral mutations) in glioblastoma, a rapidly and aggressively spreading tumor primarily in the brain and spinal cord, as well as other types of cancer. was developed to identify
IIT Madras Department of Biotechnology Prof. M. Michael Gromiha served as the principal investigator of the project. The research team includes Medha Pandey, a doctoral student at IIT Madras, as well as two IIT Madras alumni, Dr. P. Anusha, who is currently at The Ohio State University in Columbus, Ohio, and Dr. Dhanush Yesudas, who are currently together. National Institutes of Health, US
Several variables were taken into account in the development of this web server, including amino acid, di- and tri-peptide motifs, conservation scores and characteristics of position specific scoring matrices (PSSM).
The study examined 9386 driver mutations and 8728 passenger mutations in glioblastoma. In a blinded cohort of 1809 mutants, driver mutations in glioblastoma were found with an accuracy of 81.99 percent, which is better than current computational techniques. This approach completely depends on the sequence of the protein.
Pro. Gromiha summarized the fundamental findings of their study, saying that they identified important amino acid characteristics that distinguish cancer-causing mutations and achieved the highest level of accuracy for distinguishing between driver and neutral mutations. Is.
“We anticipate that this tool (GBMDriver) may help in prioritizing driver mutations in glioblastoma and assist in identifying potential therapeutic targets, thus helping in developing drug design strategies,” Professor Gromiha said.
Glioblastoma tumors have been intensively studied in the past, however, only a few therapeutic options are available, and the estimated survival rate after diagnosis is less than two years.
“We believe that the present method is helpful in prioritizing driver mutations in glioblastoma and identifying therapeutic targets,” said Medha Pandey, a PhD student at IIT Madras.
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