arXiv (All)
open access
Operationalizing open-ended biological discovery across single-cell representations
Single-cell studies are typically initiated from predefined research questions, leaving much of the biological information encoded within existing data unexplored. We formalize open-ended discovery as an analytical paradigm, in which data-derived signals are identified before biological context is i
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
- Subcellularly Resolved Single-Cell Embedding Learning with Transcriptomic data, Protein Structure and Localization Information#632894
- No wisdom in the crowd: genome annotation at the time of big data - current status and future prospects#716984
- Immune cell annotation in the single-cell studies: technologies, challenges, and integrative solutions.#150030
- Single-cell genetics identifies cell-type-specific effector genes across complex traits and diseases#282946
- Integrative machine learning and single-cell analysis identifies nicotine-related diagnostic genes and myeloid remodeling in COPD.#446129
Record · ID 820099
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