An LLM entity-tagging pipeline plus multi-agent system extracts ~6.3M nuanced records from 22.5M PubMed papers across six tasks with lower measured error than existing curated databases.
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DINO-based ViT models pretrained on HPA FOV achieve macro F1 of 0.822 zero-shot and 0.860 after fine-tuning for protein localization on OpenCell, demonstrating effective transfer from SSL pretraining.
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Self-Driving Datasets: From 20 Million Papers to Nuanced Biomedical Knowledge at Scale
An LLM entity-tagging pipeline plus multi-agent system extracts ~6.3M nuanced records from 22.5M PubMed papers across six tasks with lower measured error than existing curated databases.
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Using Deep Learning Models Pretrained by Self-Supervised Learning for Protein Localization
DINO-based ViT models pretrained on HPA FOV achieve macro F1 of 0.822 zero-shot and 0.860 after fine-tuning for protein localization on OpenCell, demonstrating effective transfer from SSL pretraining.