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EvidenceNet dataset uses LLMs to build biomedical knowledge graphs

By PulseAugur Editorial · [1 sources] ·

Researchers have developed EvidenceNet, a novel dataset and graph representation system designed for disease-specific biomedical reasoning using full-text literature. This system employs a large language model (LLM)-assisted pipeline to extract, structure, and score evidence records, linking them through semantic relations. The initial release includes EvidenceNet-HCC and EvidenceNet-CRC, containing thousands of evidence records and corresponding graph nodes and edges. Technical validation demonstrated high accuracy in extraction, entity linking, fusion, and relation typing, with downstream analyses showing the dataset's utility for question answering and graph-based tasks. AI

IMPACT Enables more sophisticated evidence-aware analysis and reuse in biomedical research through structured knowledge bases.

RANK_REASON The cluster contains a research paper detailing a new dataset and methodology for biomedical reasoning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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EvidenceNet dataset uses LLMs to build biomedical knowledge graphs

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The cluster contains a research paper detailing a new dataset and methodology for biomedical reasoning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Chang Zong, Jinyu Chen, Sicheng Lv, Si-tu Xue, Huilin Zheng, Jian Wan, Lei Zhang ·

    Building evidence-based knowledge bases from full-text literature for disease-specific biomedical reasoning

    arXiv:2603.28325v4 Announce Type: replace-cross Abstract: Biomedical knowledge resources often either preserve evidence as unstructured text or compress it into flat triples that omit study design, provenance, and quantitative support. Here we present EvidenceNet, a disease-speci…

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