IIT Madras develops AI-based tool “PIVOT” to identify cancer-causing genes
Indian Institute of Technology Madras (IIT Madras) researchers have developed an Artificial Intelligence-based tool, ‘PIVOT’, that can predict cancer-causing genes in an individual. This tool will help in devising personalised cancer treatment strategies as per the institute. The findings of the research have been published in a peer-reviewed journal called Frontiers. Cancer is an uncontrolled growth of cells that can occur due to mutations in oncogenes or tumour suppressor genes, or both. But not all mutations necessarily result in cancer. It becomes crucial to identify genes that are causing cancer to devise appropriate personalised cancer treatment strategies. ‘PIVOT,’ developed by IIT Madras researchers, is designed to predict genes that are responsible for causing cancer in an individual. The prediction is based on a model that utilises information on mutations, expression of genes, and copy number variation in genes and perturbations in the biological network due to an altered gene expression. “The tool is based on a machine learning model that classifies genes as tumour suppressor genes, oncogenes or neutral genes. The tool was able to successfully predict both the existing oncogenes and tumour-suppressor genes like TP53, and PIK3CA, among others, and new cancer-related genes such as PRKCA, SOX9 and PSMD4, “IIT Madras’ press release said. The research was led by Professor Raghunathan Rengaswamy, Dean (Global Engagement), IIT Madras, and Professor, Department of Chemical Engineering, IIT Madras, Dr. Karthik Raman, Associate Professor, Bhupat and Jyoti Mehta School of Biosciences, IIT Madras and a Core Member, Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, and Malvika Sudhakar, a Research Scholar, IIT Madras.
The prediction model utilises information on mutations, expression of genes, and copy number variation in genes and perturbations in the biological network due to an altered gene expression
The prediction model utilises information on mutations, expression of genes, and copy number variation in genes and perturbations in the biological network due to an altered gene expression
["IIT"]
["data science", "machine learning", "artificial intelligence", "programming_languages:R", "AI", "RAG", "BERT", "llm_models:BERT", "IIT", "R"]
["AI", "artificial intelligence", "machine learning", "data science", "RAG", "R", "BERT", "llm_models:BERT", "programming_languages:R"]
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