Indian Institute of Technology Madras (IIT Madras) researchers have developed a Machine Learning-based computational tool for better detection of cancer-causing tumours in the brain and spinal cord.
Called ‘GBMDriver’ (GlioBlastoma Mutiforme Drivers), this tool is publicly available online as computational techniques need to be developed to properly identify the driver mutations of different types of Cancer.
According to researchers, Glioblastoma is an aggressively growing tumour in the brain and spinal cord.
They believe that it is important to evaluate the functional consequences of variants in proteins, which are involved in Glioblastoma, to advance the therapeutic options for patients. However, functional validations to identify driver mutations (disease-causing mutations) from all the observed variants would be strenuous work.
In order to develop this web server, a variety of factors such as amino acid properties, di- and tri-peptide motifs, conservation scores, and Position Specific Scoring Matrices (PSSM) have been taken into account. This method is entirely dependent on protein sequence.
This research was led by Prof M Michael Gromiha, Department of Biotechnology, IIT Madras along with Medha Pandey, a PhD Student, IIT Madras and two IIT Madras alumni Dr P Anoosha, The Ohio State University, Columbus, US, and Dr Dhanusha Yesudhas from the National Institute of Health, US Their findings were published in the reputed peer-reviewed journal Briefings in Bioinformatics.
Explaining the key findings of their research, Prof M Michael Gromiha, Department of Biotechnology, IIT Madras, said, “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. We hope that this tool (GBMDriver) could help to prioritize driver mutations in glioblastoma and assist in identifying potential therapeutic targets, thus helping to develop drug design strategies.”
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