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Compare biological models, docking engines, and computational workflows side-by-side. Filter by access model, compute footprint, and category to reach an actionable decision table in under a minute.
| Attribute | ＋ Add tool | |||
|---|---|---|---|---|
| Category | Single-cell, Spatial & Cytometry | Single-cell, Spatial & Cytometry | Single-cell, Spatial & Cytometry | — |
| Subcategory | Single-cell Models | Conventional or Statistical Baselines | Conventional or Statistical Baselines | — |
| Company | Theis Lab | Research / method authors | Research / method authors | — |
| Access mode | Research code or publication | Confirm distribution and exact release | Confirm distribution and exact release | — |
| Licence conditions | Check code and model weights separately | NOT_INDEPENDENTLY_AUDITED | NOT_INDEPENDENTLY_AUDITED | — |
| Compute requirements | Check selected checkpoint and runtime; no GPU catalog recommendation | Version and data dependent | Version and data dependent | — |
| Biological application | Not recorded | Not recorded | Not recorded | — |
| Research workflow | Not recorded | Not recorded | Not recorded | — |
| Evidence level | Peer-reviewed primary method | OFFICIAL_DOCUMENTATION | PEER_REVIEWED_METHOD_OR_CONFERENCE | — |
| Commercial eligibility | Unknown, requires audit | NOT_INDEPENDENTLY_AUDITED | NOT_INDEPENDENTLY_AUDITED | — |
| Price | Check selected model licence | Verify access and licence | Verify access and licence | — |
| Availability ⓘ | Public research method/code | Method paper or official documentation | Method paper or official documentation | — |
| Features | Learns cell embeddings relating dissociated single-cell and spatial transcriptomics contexts., Cell latent representation, spatial-context features | Azimuth (Seurat reference mapping) scientific method, Compatible single-cell expression matrix and named atlas reference, Anchor-based reference map and predicted cell labels | Baysor scientific method, Subcellular in-situ transcript positions and optional cell stain masks, Spatial transcript-informed cell boundaries | — |
| Entry type | Research AI model / method | Classical/statistical scientific method | Classical/statistical scientific method | — |
| AI role | Published biomedical AI method | NON_AI_REFERENCE | NON_AI_STATISTICAL | — |
| Input data | scRNA expression matrix; gene vocabulary and spatial modality | Compatible single-cell expression matrix and named atlas reference | Subcellular in-situ transcript positions and optional cell stain masks | — |
| Output data | Cell latent representation; spatial-context features | Anchor-based reference map and predicted cell labels | Spatial transcript-informed cell boundaries | — |
| Validation | Requires independent dataset-specific evaluation | Published method scope; no independent BioAtlas execution or benchmark | Published method scope; no independent BioAtlas execution or benchmark | — |
| Type | Not recorded | Not recorded | Not recorded | — |
| Intended use | Not recorded | Not recorded | Not recorded | — |
| Compatibility | Not recorded | Not recorded | Not recorded | — |