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An LLM-based Knowledge Synthesis and Scientific Reasoning Framework for Biomedical Discovery

文献类型： 会议论文

第一作者： Oskar Wysocki

作者： Oskar Wysocki 1 ; Magdalena Wysocka 2 ; Danilo S. Carvalho 2 ; Alex Bogatu 2 ; Danilo Gusicuma 3 ; Maxime Delmas 3 ; Harriet Unsworth 2 ; Andre Freitas 4 ;

作者机构： 1.Idiap Research Institute, Switzerland##National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom

2.National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom

3.Idiap Research Institute, Switzerland

4.Idiap Research Institute, Switzerland##National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom##Department of Computer Science, Univ. of Manchester, United Kingdom

会议名称： [ "Annual Meeting of the Association for Computational Linguistics" , "Annual meeting of the Association for Computational Linguistics"]

主办单位：

页码： 355-364

摘要： We present BioLunar, developed using the Lunar framework, as a tool for supporting biological analyses, with a particular emphasis on molecular-level evidence enrichment for biomarker discovery in oncology. The platform integrates Large Language Models (LLMs) to facilitate complex scientific reasoning across distributed evidence spaces, enhancing the capability for harmonizing and reasoning over heterogeneous data sources. Demonstrating its utility in cancer research, BioLunar leverages modular design, reusable data access and data analysis components, and a low-code user interface, enabling researchers of all programming levels to construct LLM-enabled scientific workflows. By facilitating automatic scientific discovery and inference from heterogeneous evidence, BioLunar exemplifies the potential of the integration between LLMs, specialised databases and biomedical tools to support expert-level knowledge synthesis and discovery.

分类号： tp391

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