Large Language Model for Automating the Analysis of\nCryoprotectants

The\nrapid expansion of scientific literature necessitates developing\nefficient data extraction and analysis methods. This study presents\nan innovative approach to automating the extraction of cryoprotectant\ninformation from scientific publications using a generative pretrained\ntransformer (GPT) model integrated with a Telegram bot interface.\nOur system processes and analyzes scientific articles to identify\nand extract relevant data on cryoprotectants and bacteria, significantly\nreducing the time required for researchers to gather essential information.\nOur method optimizes the workflow for researchers in cryopreservation\nand related fields by utilizing modern artificial intelligence technologies,\nspecifically large language models. The Telegram bot, designed to\nbe user-friendly, provides a comfortable and easy platform for quick\ndata access, enhancing scientific research efficiency. The study’s\nmethodology involves data preparation, algorithm development, and\nsystem validation using a substantial data set of scientific articles.\nResults demonstrate the model’s capability to accurately recognize\nand extract critical information, although some limitations in term\nspecificity were noted. Our findings suggest that further refinement\nand training of the model can enhance its accuracy and reliability\nfor specialized scientific applications.