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Genomics & Bioinformatics · Bi-directional Mamba Genomic Models

Caduceus

By Stanford University & Princeton University →

Bi-directional state space model for long-range DNA sequence modeling

Caduceus introduces bi-directional Mamba state space architectures with reverse-complement equivariance, outperforming transformers on long genomic sequence prediction while reducing compute footprint.

Bi-directional state space (Mamba) architecture overcoming causal autoregressive limitationsGuaranteed reverse-complement (RC) equivariance essential for double-stranded DNA modelingSub-quadratic scaling allowing 131k base pair context on single standard GPUsSuperior performance on variant effect and chromatin accessibility benchmarks
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Overview

Caduceus introduces bi-directional Mamba state space architectures with reverse-complement equivariance, outperforming transformers on long genomic sequence prediction while reducing compute footprint.

Information checked against an official source; not a hands-on test. Source · Last reviewed: 20/09/2026, 11:10:15

Key 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

Academic Context & Research Evidence

Biological & Workflow Fit

Biological Application
Long-range gene regulation, promoter-enhancer mapping, and variant impact scoring
Research Workflow
Genomic DNA sequence -> predict regulatory activity with RC equivariance
Compute & Hardware
1x GPU (>= 16GB VRAM)
Licensing & Academic Use
Apache 2.0
Documented Evidence
View validation publication / source ↗

Cite this Tool

Use this citation format when referencing Caduceus in scientific publications and benchmark papers.

@software{caduceus_2026,
  title = {{Caduceus}},
  author = {{Stanford University & Princeton University}},
  year = {2026},
  url = {https://github.com/kuleshov-group/caduceus},
  note = {Indexed on aibioatlas - AI for Biology and Drug Discovery}
}

Peer-Reviewed Literature & Preprints

Live scientific citations streamed from Europe PMC and PubMed for Caduceus.

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Technical / Product Information

Missing values mean the catalog has no recorded information. They do not mean a feature is absent.

Entry typeAI Model
Access modeOpen Source
AI roleEquivariant State Space Modeling
Input dataNot recorded
Output dataNot recorded
Licence conditionsApache 2.0
Commercial eligibilityOpen source release
Compute requirements1x GPU (>= 16GB VRAM)
ValidationNot recorded
TypeBi-directional Mamba genomic model
Intended useNot recorded
CompatibilityNot recorded
ManufacturerStanford University & Princeton University
Biological applicationLong-range gene regulation, promoter-enhancer mapping, and variant impact scoring
Research workflowGenomic DNA sequence -> predict regulatory activity with RC equivariance
Evidence levelPeer-reviewed research (ICML 2024)
Integration evidencehttps://github.com/kuleshov-group/caduceus
Laboratory handoffInforms non-coding variant prioritization for CRISPR functional genomics
AvailabilityAvailable on GitHub and Hugging Face
Price / accessFree Open Source

Research fit & compatibility

No software–hardware integration has been verified for this entry yet. Explore documented research workflows.

Gallery & resources

No licensed product photograph recorded for Caduceus yet. Visit the official resource below for manufacturer imagery.

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FAQ

Where is this product available?

Available on GitHub and Hugging Face

How is pricing handled?

Prices reflect the source at its last check. Confirm current pricing and regional availability on the official site.

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Peer Reviews & Community Ratings

Feedback from researchers and computational biologists evaluating Caduceus.

5.0
★★★★★Based on 0 researcher evaluations
Biological Accuracy
4.8/5
Ease of Installation
4.3/5
Documentation & Code
4.6/5