An LLM-based Knowledge Synthesis and Scientific Reasoning Framework for Biomedical Discovery
Oskar Wysocki, Danilo Carvalho, Alex Bogatu, Andre Freitas
Abstract
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.
BibTeX
@inproceedings{wysocki-etal-2024-llm,
title = "An {LLM}-based Knowledge Synthesis and Scientific Reasoning Framework for Biomedical Discovery",
author = "Wysocki, Oskar and
Magdalena.wysocka@cruk.manchester.ac.uk, Magdalena.wysocka@cruk.manchester.ac.uk and
Carvalho, Danilo and
Bogatu, Alex and
Danilo.miranda@idiap.ch, Danilo.miranda@idiap.ch and
Maxime.delmas@idiap.ch, Maxime.delmas@idiap.ch and
Harriet.unsworth@cruk.manchester.ac.uk, Harriet.unsworth@cruk.manchester.ac.uk and
Freitas, Andre",
editor = "Cao, Yixin and
Feng, Yang and
Xiong, Deyi",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-demos.34/",
doi = "10.18653/v1/2024.acl-demos.34",
pages = "355--364"
}