SANE is a new schema-aware benchmark paradigm for text-to-SQL evaluation that demonstrates few-shot LLMs with structured prompting can generate accurate queries on constrained biological data schemas without fine-tuning.
CellProfiler: image analysis software for identifying and quantifying cell phenotypes
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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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SANE Schema-aware Natural-language Evaluation of Biological Data
SANE is a new schema-aware benchmark paradigm for text-to-SQL evaluation that demonstrates few-shot LLMs with structured prompting can generate accurate queries on constrained biological data schemas without fine-tuning.
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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.