arXiv (All)
open access
Can LLMs Reliably Annotate Bioassay Metadata to Improve Data Readiness?
The emergence of foundation models for molecular property prediction requires a high degree of AI data readiness, including reliable metadata annotation. However, both public repositories and industrial screening databases suffer from missing, inconsistent, or conflated assay annotations. In this wo
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Can LLMs Reliably Annotate Bioassay Metadata to Improve Data Readiness?#1169663
- Semantic knowledge improves molecular machine learning for chemical toxicity prediction.#422908
- AI-readiness Criteria for Biomedical Data#240474
- MetaPerch: Learning from metadata for bioacoustics foundation models#370314
- Development of Metadata Schemas For Collaborative Research Centers#206006
Record · ID 1156887
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive