BioBERT
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Related Articles from SNS
Correlation Is Not Enough: Embedding Human Metadata for Individual Causal Discovery
arXiv:2606.09672v1 Announce Type: new Abstract: Ask a pretrained biomedical language model whether "cortisol 28 ug/dL" and "stock-market volatility" are related, and it returns a cosine similarity of 0.83 on a scale where 1.0 means identical. The two share no mechanism. This is not a corner case: every off-the-shelf biomedical encoder we tested (BioBERT, PubMedBERT, BioM-ELECTRA) scores unrelated cross-domain pairs between 0.76 and 0.92 when the answer should be near zero.
Generalistic or Specific Embeddings, Which is Better? An Empirical Study on Search for Clinical Coding in Non-English Languages
arXiv:2605.30529v1 Announce Type: new Abstract: Sentence-embedding models for semantic search are overwhelmingly developed and evaluated on English corpora. When applied to clinical retrieval in other languages -- particularly retrieval of ICD-10-CM / CIE-10 codes -- recall degrades in ways often masked by aggregate benchmarks. We study whether large generative language models can serve as data factories to close this gap.
Ignet 2.0 and Vignet: An Ontology-Driven Web Platform for Biomedical Gene Interaction Discovery and Visualization
Background: The expansion of biomedical literature demands systematic ontology-guided discovery of gene interactions, vaccine mechanisms, drug associations, and adverse events. Existing platforms such as STRING, DisGeNET, and PubTator fall short of providing a unified, freely accessible system that integrates ontology-based semantic interaction classification, vaccine-focused heterogeneous network construction, and Artificial Intelligence-assisted evidence retrieval. Ignet 2.0 and Vignet are...
When Large Language Models Fail in Healthcare: Evaluating Sensitivity to Prompt Variations
arXiv:2606.07237v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly used in healthcare for tasks such as clinical question answering, diagnosis support, and report summarization. Despite their promise, these models remain highly sensitive to subtle prompt perturbations, both lexical and syntactic, posing serious risks in safety-critical clinical applications.