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Confronta modelli biologici, motori di docking e flussi di calcolo affiancati. Filtra per modello di accesso, impronta di calcolo e categoria per ottenere una tabella decisionale attuabile in meno di un minuto.
| Attributo | Genomics & Sequence Modeling CaduceusStanford University & Princeton UniversityCodice aperto🖥️ GPU RTX | ＋ Aggiungi strumento | ||
|---|---|---|---|---|
| Category | Genomics & Sequence Modeling | Genomics & Bioinformatics | Genomics & Bioinformatics | — |
| Subcategory | Bi-directional Mamba Genomic Models | Regulatory Genomics | Conventional or Statistical Baselines | — |
| Company | Stanford University & Princeton University | Google DeepMind | Research / method authors | — |
| Access mode | Open Source | Research code or publication | Confirm distribution and exact release | — |
| Licence conditions | Apache 2.0 | Check code and model weights separately | NOT_INDEPENDENTLY_AUDITED | — |
| Compute requirements | 1x GPU (>= 16GB VRAM) | Check selected checkpoint and runtime; no GPU catalog recommendation | Version and data dependent | — |
| Biological application | Long-range gene regulation, promoter-enhancer mapping, and variant impact scoring | Non registrato | Non registrato | — |
| Research workflow | Genomic DNA sequence -> predict regulatory activity with RC equivariance | Non registrato | Non registrato | — |
| Evidence level | Peer-reviewed research (ICML 2024) | Peer-reviewed primary method | PEER_REVIEWED_METHOD_OR_CONFERENCE | — |
| Commercial eligibility | Open source release | Unknown, requires audit | NOT_INDEPENDENTLY_AUDITED | — |
| Price | Free Open Source | Check selected model licence | Verify access and licence | — |
| Availability ⓘ | Available on GitHub and Hugging Face | Public research method/code | Method paper or official documentation | — |
| Features | Bi-directional state space (Mamba) architecture overcoming causal autoregressive limitations, Guaranteed reverse-complement (RC) equivariance essential for double-stranded DNA modeling, Sub-quadratic scaling allowing 131k base pair context on single standard GPUs, Superior performance on variant effect and chromatin accessibility benchmarks | Predicts genomic regulatory tracks and predicted functional effects of noncoding DNA variants., Genomic regulatory track, sequence-variant contrast | bcftools scientific method, Reference genome with aligned short-read input and correct ploidy assumptions, VCF/BCF variant calls and variants QC operations | — |
| Entry type | AI Model | Research AI model / method | Classical/statistical scientific method | — |
| AI role | Equivariant State Space Modeling | Published biomedical AI method | NON_AI_STATISTICAL | — |
| Input data | Non registrato | DNA sequence interval; alleles and tissue/assay ontology | Reference genome with aligned short-read input and correct ploidy assumptions | — |
| Output data | Non registrato | Genomic regulatory track; sequence-variant contrast | VCF/BCF variant calls and variants QC operations | — |
| Validation | Non registrato | Requires independent dataset-specific evaluation | Published method scope; no independent BioAtlas execution or benchmark | — |
| Type | Bi-directional Mamba genomic model | Non registrato | Non registrato | — |
| Intended use | Non registrato | Non registrato | Non registrato | — |
| Compatibility | Non registrato | Non registrato | Non registrato | — |
| Manufacturer | Stanford University & Princeton University | Non registrato | Non registrato | — |
| Integration evidence | https://github.com/kuleshov-group/caduceus | Non registrato | Non registrato | — |
| Laboratory handoff | Informs non-coding variant prioritization for CRISPR functional genomics | Non registrato | Non registrato | — |