A machine learning (ML)-based computational technique has been created by researchers at the Indian Institute of Technology (IIT) Madras to improve the identification of cancerous tumours in the brain and spinal cord.

The brain and spinal cord are both home to the quickly spreading glioblastoma cancer.

Despite the fact that research has been done to comprehend this cancer, there are still few therapeutic options available, and the predicted survival time after diagnosis is less than two years.

To specifically detect driver mutations and passenger mutations in glioblastoma, the technology known as aGBMDriver (GlioBlastoma Mutiforme Drivers) was created. The tool is accessible to everyone online.

Passenger or “hitchhiker” mutations, which account for over 97% of all carcinogenic mutations, are typically characterised as mutations that do not promote cell proliferation and tumour growth.

“We have identified the important amino acid features for identifying cancer-causing mutations and achieved the highest accuracy for distinguishing between driver and neutral mutations,” said Prof. M. Michael Gromiha, Department of Biotechnology at IIT Madras, in a statement.

“We hope that this tool (GBMDriver) could help to prioritise driver mutations in glioblastoma and assist in identifying potential therapeutic targets, thus helping to develop drug design strategies,” Gromiha added.

Also Read:- New technique to detect early-stage cancer tumour cells

The scientists examined 8,728 passenger mutations and 9,386 driver mutations in glioblastoma in order to create this web server. In a blind set of 1809 mutants, driver mutations in glioblastoma were detected with an accuracy of 81.99%, which is superior than current computational techniques. The protein sequence is the sole determinant in this technique.

The peer-reviewed journal Briefings in Bioinformatics published their findings.

Other diseases can also benefit from using the ML tool. The technique might be one of the crucial factors in determining a disease’s prognosis. It is a useful tool for locating pharmacological targets that are specific to a mutation while creating treatment plans.

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