\n \n What are large language models (LLMs)?\u00a0\n <\/h3>\n \n \n
LLMs are a type of artificial intelligence system trained on vast amounts of textual data. By processing and learning from this data, these models can generate coherent and contextually relevant text across a wide range of topics. LLMs can understand and produce human-like text, making them valuable tools for tasks such as content creation, answering questions, and natural language understanding. <\/span><\/p>\n <\/div>\n<\/article>\n\n\n\n Academic papers are currently being published at an unprecedented pace, and the challenge of pulling out relevant information has never been greater. Andrew Green, ARISE Fellow at EMBL-EBI<\/a> has been using LLMs to streamline the data curation for EMBL-EBI\u2019s database for non-coding RNAs, RNAcentral<\/a>. <\/p>\n\n\n\n To do this, Green has successfully developed a tool to scrape scientific articles that mention specific RNA identifiers. These sentences are then fed into GPT-3.5, which generates concise, coherent summaries about the RNA of interest. These summaries describe key details such as the RNA’s functions, its involvement in diseases, and the organisms in which it has been studied.<\/p>\n\n\n\n “One of the intriguing features of using LLMs is the accuracy and contextual understanding they bring into the summarisation process,” said Green. “We’ve seen the model accurately decipher acronyms in a given context and even self-correct its errors when asked to fact-check its summaries.”<\/p>\n\n\n\n To ensure the summaries generated are robust, they go through multiple rounds of validation, and are then rated for quality before appearing in the RNAcentral database. The summaries serve as quick references for scientists to better understand a particular RNA, and also include clickable citations to the original articles on Europe PMC<\/a>.<\/p>\n\n\n\n “It’s crucial to remember that LLMs don’t inherently know the difference between what’s real and what’s fabricated,\u201d added Green. \u201cIn the scientific community, where factual accuracy is paramount, this could be a major concern. Models can sometimes ‘hallucinate’ details that aren’t in the original text. To mitigate this, we have put multiple validation rounds in place. This, combined with constant human oversight, ensures that the information presented is both accurate and reliable.”<\/p>\n\n\n\n At the heart of this approach is an automated method for extracting and summarising valuable information from a multitude of academic articles. This means that this work can also be applied to many other EMBL-EBI resources. Once fully developed and implemented, this automated process for curation serves to aid the work of many of EMBL-EBI\u2019s curators, acting as a first filter in the lengthy process of data collection and interpretation. <\/p>\n\n\n\n Another aspect of the EMBL-EBI database pipelines that can benefit from LLMs is data annotation. Melanie Vollmar is an ARISE Fellow at EMBL-EBI<\/a> with a strong background in structural biology and a growing expertise in machine learning. As part of her fellowship, she is looking at how to fully automate the extraction of functional information about proteins from academic papers using LLMs. <\/p>\n\n\n\n Her project focuses on gathering structural information from the Protein Data Bank in Europe (PDBe) and supplementing it with related academic publications from Europe PMC. This curated information is then mined for specific functional details, which are mapped back onto the protein sequences listed in UniProt<\/a>. <\/p>\n\n\n\n Until now, curating literature for functional annotations followed a purely manual approach supplemented by traditional text mining methods. LLMs, designed to grasp the intricacies of human language, can parse through vast amounts of scientific literature, weigh contrasting opinions, and generate complex text-based outputs. <\/p>\n\n\n\n This capability can bring in a new era of data enrichment, as these models help to extract more detailed and contextually rich information from existing biological literature at an accelerated pace. At no point is such a model intended to replace the human biocurator who is required to provide a critical view on the produced output.<\/p>\n\n\n\n \u201cWith automation, not only do we increase the pace at which we can annotate data, but we also enrich the quality of that data, offering a more comprehensive resource for our users,\u201d said Vollmar. \u201cOur focus now is on protein structures, but the beauty of our approach is its adaptability: the methods we’re developing could easily be transplanted onto other types of biological data, elevating the annotation process across the board.”<\/p>\n\n\n\n Europe PMC is EMBL-EBI\u2019s home of scientific literature, and after many years of serving the scientific community, the resource remains an intuitive and powerful search tool to help users stay on the cutting edge of science. Many of the database’s functionalities rely on literature curation, which involves scanning through dense academic material to extract essential information. <\/p>\n\n\n\n Santosh Tirunagari, Senior Machine Learning Developer<\/a> at EMBL-EBI is leveraging the capabilities of LLMs to accelerate the curation of scientific literature within Europe PMC. He and others in the team have developed specialised named entity recognition models, which are fine-tuned versions of existing LLMs. These sophisticated tools are designed to automatically identify critical scientific entities such as genes, proteins, diseases, and chemicals in research papers and patents.<\/p>\n\n\n\n Using this approach helps to side-step the high computational costs of developing a language model from scratch, which could require dozens of GPUs and extensive training time. By concentrating on the fine-tuning phase, Tirunagari has been able to adapt these powerful language models to specific tasks relevant to scientific curation. This maximises efficiency while achieving high levels of accuracy.<\/p>\n\n\n\n In one of his models, Tirunagari also uses an innovative \u2018human-in-the-loop’ methodology for model training. Beginning with a limited dataset, the fine-tuned models undergo further adjustments using additional scientific papers. Human curators then verify the model’s findings, enabling an iterative feedback loop that continually improves the model\u2019s accuracy.<\/p>\n\n\n\n “Large language models have been a game-changer in our efforts to automate the complex task of scientific curation. By fine-tuning these models, we’ve been able to develop highly specialised tools that can sift through vast amounts of scientific literature and patents to identify key entities such as genes, organisms, proteins, and diseases with impressive accuracy,\u201d said Tirunagari. \u201cThis not only accelerates our work but also opens up new possibilities for collaborations, like our ongoing partnership with Open Targets to use these models to aid drug discovery.”<\/p>\n\n\n\n Ontologies are structured, hierarchical classifications that are widely used for standardising diseases. Current practices for ontology mapping rely heavily on manual curation, making it a time-consuming and error-prone task. To address these issues, Kirill Tsukanov, Senior Bioinformatician at EMBL-EBI<\/a>, has developed a new method for ontology mapping using openly-available, GPT-based language models. <\/p>\n\n\n\nTransforming data curation <\/strong><\/h2>\n\n\n\n
Accelerating annotation <\/strong><\/h2>\n\n\n\n
Fine-tuning existing LLMs <\/strong><\/h2>\n\n\n\n
A novel approach to ontology mapping <\/strong><\/h2>\n\n\n\n