Researchers at the Indian Institute of Technology-Madras (IIT Madras) have developed PIVOT, a predictable artificial intelligence-based tool. cancer-Induces a gene in an individual. This early information not only helps prevent cancer, but can even devise personalized cancer treatment strategies.
Gene predictions that cause cancer are based on models that utilize information about mutations, gene expression, gene copy number polymorphisms, and changes in gene expression that disrupt biological networks.
The study was led by Professor Raghunathan Rengaswamy, Dean of the Faculty of IIT Madras, Dr. Karthik Raman, Professor of Chemical Engineering of IIT Madras, Associate Professor of Bhupat and Jyoti Mehta Faculty of Biological Sciences, IIT Madras and Core. Malvika Sudhakar, a member of the Robert Bosch Data Science and Artificial Intelligence Center (RBCDSAI), IIT Madras, and a researcher at IIT Madras.The survey results are Published in peer-reviewed journals The frontier of genetics.
Emphasizing the importance of research, Dr. Carsic Raman of IIT Madras, a core member of RBCDSAI, said: As cancer treatments increasingly move to personalized medicine, such models built to accurately identify differences between patients can be very helpful. “
This tool is based on a machine learning model that classifies genes as tumor suppressors, oncogenes, or neutral genes. The tool was able to successfully predict both existing oncogenes and tumor suppressor genes such as TP53 and PIK3CA, as well as new oncogenes such as PRKCA, SOX9 and PSMD4.
Regarding the importance of providing personalized cancer treatments, IIT Madras researcher Malvika Sudhakar said: PIVOT helps push these limits and provides the prospect of research based on the identified genes. “
Current cancer treatments are known to be detrimental to the patient’s overall health. Knowledge of the genes involved in the development and progression of a patient’s cancer can help determine the best combination of medicines and treatments for the patient’s recovery. Although there are tools available to identify personalized oncogenes, we use unsupervised learning to predict scenarios based on the presence or absence of mutations in cancer-related genes. However, this study is the first to use supervised learning and takes into account the functional effects of mutations.
Researchers at IIT Madras have built AI predictive models for three types of cancer, including breast invasive cancer, colon adenocarcinoma, and lung adenocarcinoma. They plan to spread it to more types of cancer. The team is also working on a list of genes responsible for personalized cancer that will help identify the right drug for the patient.
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