Detecting Brain Tumors with Machine Learning

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Researchers at the Indian Institute of Technology (IIT) Madras have developed a machine learning (ML) technique to improve the identification of cancer-causing tumours in the brain and spinal cord. This technology, called aGBMDriver (GlioBlastoma Mutiforme Drivers), is available to all online and helps to detect driver and passenger mutations in glioblastoma more accurately.

The protein sequence is the sole main factor for making use of this technique, Professor M. Michael Gromiha from the Department of Biotechnology at IIT Madras explained. He went on to explain that the team has identified important amino acid features for identifying cancer-causing mutations and achieved a high accuracy for distinguishing between driver and passenger mutations.

The scientists examined 8,728 passenger mutations and 9,386 driver mutations in glioblastoma 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 higher than current computational techniques.

The researchers’ findings were published in the peer-reviewed journal Briefings in Bioinformatics. This technology could benefit other diseases as well, and may be crucial in determining a disease’s prognosis. It may expedite the process in locating pharmacological targets with greater accuracy and creating treatment plans. According to Medha Pandey, a PhD student at IIT Madras, the method “could help to prioritize driver mutations in glioblastoma and assist in identifying therapeutic targets.”

The Indian Institute of Technology Madras is a premier public educational and research institution in India. Founded in 1959, it is renowned for the quality of its graduates and its world-class research initiatives that prioritize the development of cutting-edge technology.

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Professor M. Michael Gromiha is a renowned biotechnology scientist, who has been working in the field of bioinformatics since 1999. He is currently working on a project related to identifying driver mutations in glioblastoma, for which his team has created the web server “aGBMDriver”. His findings have been published in some of the highest-impact journals in the world.

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