Yuki Yamagata

Shanshan Liu, Noriki Nishida, Fei Cheng et al.

Generalization to unseen concepts is a central challenge due to the scarcity of human annotations in Mention-agnostic Biomedical Concept Recognition (MA-BCR). This work makes two key contributions to systematically address this issue. First, we propose an evaluation framework built on hierarchical concept indices and novel metrics to measure generalization. Second, we explore LLM-based Auto-Labeled Data (ALD) as a scalable resource, creating a task-specific pipeline for its generation. Our research unequivocally shows that while LLM-generated ALD cannot fully substitute for manual annotations, it is a valuable resource for improving generalization, successfully providing models with the broader coverage and structural knowledge needed to approach recognizing unseen concepts. Code and datasets are available at https://github.com/bio-ie-tool/hi-ald.

Shanshan Liu, Noriki Nishida, Rumana Ferdous Munne et al.

Recognizing biomedical concepts in the text is vital for ontology refinement, knowledge graph construction, and concept relationship discovery. However, traditional concept recognition methods, relying on explicit mention identification, often fail to capture complex concepts not explicitly stated in the text. To overcome this limitation, we introduce MA-COIR, a framework that reformulates concept recognition as an indexing-recognition task. By assigning semantic search indexes (ssIDs) to concepts, MA-COIR resolves ambiguities in ontology entries and enhances recognition efficiency. Using a pretrained BART-based model fine-tuned on small datasets, our approach reduces computational requirements to facilitate adoption by domain experts. Furthermore, we incorporate large language models (LLMs)-generated queries and synthetic data to improve recognition in low-resource settings. Experimental results on three scenarios (CDR, HPO, and HOIP) highlight the effectiveness of MA-COIR in recognizing both explicit and implicit concepts without the need for mention-level annotations during inference, advancing ontology-driven concept recognition in biomedical domain applications. Our code and constructed data are available at https://github.com/sl-633/macoir-master.

Yuki Yamagata, Koji Kyoda, Hiroya Itoga et al.

Advanced bioimaging technologies have enabled the large-scale acquisition of multidimensional data, yet effective metadata management and interoperability remain significant challenges. To address these issues, we propose a new ontology-driven framework for the Systems Science of Biological Dynamics Database (SSBD) that adopts a two-tier architecture. The core layer provides a class-centric structure referencing existing biomedical ontologies, supporting both SSBD:repository -- which focuses on rapid dataset publication with minimal metadata -- and SSBD:database, which is enhanced with biological and imaging-related annotations. Meanwhile, the instance layer represents actual imaging dataset information as Resource Description Framework individuals that are explicitly linked to the core classes. This layered approach aligns flexible instance data with robust ontological classes, enabling seamless integration and advanced semantic queries. By coupling flexibility with rigor, the SSBD Ontology promotes interoperability, data reuse, and the discovery of novel biological mechanisms. Moreover, our solution aligns with the Recommended Metadata for Biological Images guidelines and fosters compatibility. Ultimately, our approach contributes to establishing a Findable, Accessible, Interoperable, and Reusable data ecosystem within the bioimaging community.