IITM develops AI tool for personalized cancer diagnosis

IITM develops AI tool for personalized cancer diagnosis

Chennai, July 7: 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 ultimately help in devising personalized cancer treatment strategies.

According to World Health Organization, cancer is a leading cause of death worldwide and accounted for nearly one in six deaths in 2020.

Cancer is an uncontrolled growth of cells that can occur due to mutations in oncogenes or tumor suppressor genes or both. However, not all mutations necessarily result in cancer. Therefore, it is important to identify genes that are causing cancer to devise appropriate personalized 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 utilizes 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 research was led by Prof. 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 Ms. Malvika Sudhakar, a Research Scholar, IIT Madras.

The findings of the research have been published in a peer-reviewed journal Frontier in Genetics.

Highlighting the significance of the Research, Dr. Karthik Raman, Core Member, RBCDSAI, IIT Madras, said, "Cancer, being a complex disease, cannot be dealt with in a one-treatment-fits-all fashion. As cancer treatment increasingly shifts towards personalized medicine, such models that build toward pinpointing differences between patients can be very useful."

The tool is based on a machine learning model that classifies genes as tumor suppressor genes, oncogenes, or neutral genes. The tool was able to successfully predict both the existing oncogenes and tumor-suppressor genes like TP53, and PIK3CA, among others, and new cancer-related genes such as PRKCA, SOX9, and PSMD4.



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