eScholarship
metadata only
Achieving Multi-scale Cell Morphology Clustering using Machine Learning
Understanding the heterogeneity of cell types and gene expression in complex tissues is crucial for advancing single cell genomics. Spatial transcriptomics enhances this understanding by adding spatial context, enabling a more comprehensive view of cellular function and organization. Additionally, t
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
- Clump sequencing exposes the spatial expression programs of intestinal secretory cells#818527
- CAdir: Joint clustering of cells and genes for single-cell transcriptomics with visualization-driven cluster quality assessment#327597
- MARC: Morphology-Aware Regression of Consensus for Cell Segmentation in Subcellular Spatial Transcriptomics#1071433
- Identifying breast cancer subtypes and exploring spatial expression patterns of the key subtype-specific gene based on pathological images, single-cell sequencing, and spatial transcriptomic data.#610319
- Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation#1013626
Record · ID 983583
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