Publications by authors named "C Arighi"

Article Synopsis
  • Large language models (LLMs) depend on high-quality biomedical annotations for training, which are usually created through costly and slow human efforts.
  • LLMs can streamline the curation process, creating a feedback loop where improvements in one area aid the other.
  • The workshop will explore both the benefits and challenges of using LLMs in biomedical annotation and curation, highlighting the current landscape and implications for the future.
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Dynamic changes in protein glycosylation impact human health and disease progression. However, current resources that capture disease and phenotype information focus primarily on the macromolecules within the central dogma of molecular biology (DNA, RNA, proteins). To gain a better understanding of organisms, there is a need to capture the functional impact of glycans and glycosylation on biological processes.

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Large Language Models (LLMs) are a type of artificial intelligence that has been revolutionizing various fields, including biomedicine. They have the capability to process and analyze large amounts of data, understand natural language, and generate new content, making them highly desirable in many biomedical applications and beyond. In this workshop, we aim to introduce the attendees to an in-depth understanding of the rise of LLMs in biomedicine, and how they are being used to drive innovation and improve outcomes in the field, along with associated challenges and pitfalls.

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Motivation: Figures in biomedical papers communicate essential information with the potential to identify relevant documents in biomedical and clinical settings. However, academic search interfaces mainly search over text fields.

Results: We describe a search system for biomedical documents that leverages image modalities and an existing index server.

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