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BioChatter: making large language models accessible for biomedical research

A team led by researchers at EMBL's European Bioinformatics Institute (EMBL-EBI), in collaboration with a PhD candidate in the Christiaen group at the Michael Sars Centre, developed an open-source large language model (LLM) framework designed for custom biomedical research.

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Karen Arnott/EMBL-EBI

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Large language models (LLMs) have transformed how many of us work, from supporting content creation and coding to improving search engines. However, the lack of transparency, reproducibility, and customisation of LLMs remains a challenge that restricts their widespread use in biomedical research. 

What are large language models (LLMs)? 

Large Language Models (LLMs) are artificial intelligence systems designed to process and generate human-like text by leveraging vast amounts of training data. They are capable of performing a wide range of tasks such as text generation, language translation, summarising text, answering questions, and more.

For biomedical researchers, optimising LLMs for a specific research question can be daunting, because it requires programming skills and machine learning expertise. Such barriers have reduced the adoption of LLMs for many research tasks, including data extraction and analysis.

A new publication in Nature Biotechnology introduces BioChatter to help overcome these limitations. BioChatter is an open-source Python framework for deploying LLMs in biomedical research, in line with open science principles. In order to address the concerns of privacy and reproducibility often associated with commercial LLMs, BioChatter offers a framework for researchers seeking transparency and flexibility in their LLM workflows.

"Large language models hold immense potential to transform biomedical research by making complex data and analysis tasks more accessible," said Julio Saez-Rodriguez, Head of Research at EMBL’s European Bioinformatics Institute (EMBL-EBI), and Professor on leave at Heidelberg University. "However, to make the most of this technology for biomedical research, we need tools that prioritise transparency and reproducibility. BioChatter bridges this gap, allowing researchers to integrate LLM capabilities into many biomedical research tasks."

Interfacing with biomedical knowledge graphs and software

BioChatter can be adapted to specific research areas to pull data from biomedical databases and literature. Further, instructing LLMs to use external software via the BioChatter API-calling functionality enables real-time access to up-to-date information and integration with bioinformatics tools. 

A key feature of BioChatter is its ability to integrate with BioCypher-built knowledge graphs – networks that link biomedical data such as genetic mutations, drug-disease associations, and other clinical information. These graphs help researchers analyse complex datasets to help identify genetic variations in disease or understand drug mechanisms.

"BioChatter is designed to lower the barriers for biomedical researchers using large language models by providing an open, transparent framework that can be adapted to different research needs," said Sebastian Lobentanzer, Postdoctoral Researcher at the Heidelberg University Hospital and incoming Principal Investigator at Helmholtz Munich. "Our goal is to help scientists focus on their research while leaving the technical complexities to the platform.”

Noah Bruderer, PhD candidate in the group of Lionel Christiaen at the Michael Sars Centre, contributed to BioChatter with a tool bridging LLMs and open databases. He also spoke about the practical benefits of the framework. "I want researchers to be able to maximize their contribution to knowledge without getting lost in navigating the fragmented ocean of public databases”, he said. “This is why I was very excited when I got the first results showing that LLMs can be leveraged to reliably answer biological questions by accessing these repositories.”

Real-world applications 

The next step for BioChatter is trialling its integration into life science databases. The team behind BioChatter is working closely with Open Targets, a public-private partnership that includes EMBL-EBI and uses human genetics and genomics data for systematic drug target identification and prioritisation. Integrating BioChatter into the Open Targets Platform could help streamline how users access and use biomedical data from the platform.

The team is also developing BioGather, a complementary system designed to extract information from other clinical data types, including genomics, medical notes, and images. By helping to analyse and align these data types, BioGather will help researchers address complex problems in personalised medicine, disease modelling, and drug development.