(Source: https://timesofindia.indiatimes.com/life-style/health-fitness/health-news)
At the same time
when cancer is claiming close to 2 million lives globally and is accountable
for one in six deaths, researchers at the Indian Institute of Technology (IIT
Madras) have come up with an AI-backed tool to predict cancer-causing genes.
Known as PIVOT, this tool has been created by a group of researchers led by
Prof. Raghunathan Rengaswamy, Dean (Global Engagement) and Professor,
Department of Chemical Engineering, Dr. Karthik Raman; Associate Professor,
Bhupat and Jyoti Mehta School of Biosciences and a Core Member, Robert Bosch
Centre for Data Science and Artificial Intelligence (RBCDSAI), and Ms. Malvika
Sudhakar, a Research Scholar, an official statement says.
How does PIVOT function?
The AI prediction model has been built for three different types of cancer
including Breast Invasive Carcinoma, Colon Adenocarcinoma and Lung
Adenocarcinoma.
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The researchers claim, the mechanism behind the prediction of cancerous genes
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 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, an official statement from the institute says.
Cancer is an uncontrolled growth of cells that can occur due to mutations in oncogenes or by tumor suppressor genes or both. Since all mutations do not necessarily result in cancer, therefore, it is important to identify genes that are causing cancer to devise appropriate personalized cancer treatment strategies, the researchers have said.
"Although there are tools available to identify personalized cancer genes, they use unsupervised learning and predict based on presence and absence of mutations in cancer-related genes. This study, however, is the first one to use supervised learning and takes into account the functional impact of mutations while making predictions," the institute has said.
The team is also working on a list of personalized cancer-causing genes that can help in identifying the suitable drug for patients based on their personalized cancer profile, the institute adds.
