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- W4386114177 abstract "Prognostic signatures play an important role in clinical research, offering insights into the potential health outcomes of patients and guiding therapeutic decisions. Although single-gene prognostic biomarkers are valuable, multi-gene prognostic signatures offer deeper insights into disease progression. In this paper, we propose MulMarker, a framework that harnesses large language models (LLMs, such as GPT) to identify and evaluate multi-gene prognostic signatures across various diseases. MulMarker comprises three core modules: a GPT-driven chatbot for addressing user queries, a module for identifying multi-gene prognostic signatures, and a module for generating tailored reports. Using MulMarker, we identified a cell cycle-related prognostic signature that consists of CCNA1/2, CCNB1/2/3, CCNC, CCND1/2/3, CCNE1/2, CCNF, CCNG1/2, and CCNH. When stratifying patients based on the prognostic signature, we found that patients in the low-risk group have a higher survival rate than those in the high-risk group. Overall, MulMarker offers an approach to the identification and validation of potential multi-gene prognostic signatures. By employing GPT to address user queries and generate tailored reports, it underscores the potential of integrating cutting-edge Artificial Intelligence (AI) solutions into prognostic research. We release the code of MulMarker at https://github.com/Tina9/MulMarker." @default.
- W4386114177 created "2023-08-24" @default.
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- W4386114177 date "2023-08-22" @default.
- W4386114177 modified "2023-09-23" @default.
- W4386114177 title "MulMarker: a GPT-assisted comprehensive framework for identifying potential multi-gene prognostic signatures" @default.
- W4386114177 doi "https://doi.org/10.48550/arxiv.2308.11349" @default.
- W4386114177 hasPublicationYear "2023" @default.
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