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Projects
Clinical feature-based mutation risk prediction in NSCLC using a GPT-derived model
SJ Cherulil, G Kormath Veetil, S Kombath Payini, A Chandrashekharan, S Valson, SK Ch, F Zereen, A Mohan The gold standard for the detection of actionable mutations remains the comprehensive genomic analysis using NGS which is resource intensive and in an LMIC and adds financial toxicity. LLMs (Large Language Models) have shown remarkable capability to interpret large volumes of data and the same has been used for analysis of real world data. The present study, aimed to assess the accuracy of prediction of the probabilities of occurence of common mutations using a GPT derived Model.
