Helical Platform
ChatGPT pluginSingle-cell foundation models
Helical lets you run biological foundation models on your own single-cell data, from the chat you're already in.
Upload an .h5ad dataset into your private Helical project, pick a model — scGPT, Geneformer, TranscriptFormer, Cell2Sentence or Nicheformer — and compute embeddings on Helical's GPUs. Follow the run while it goes, read the error if it fails, and download the embeddings when it finishes.
Nothing starts on its own. A run is quoted and queued first, and only begins once you approve it in a second, separate step. Every tool works inside the one project your account owns.
Runs use the credits in your existing Helical account.
- Featured
- No
not on the featured shelf
Movement
From our daily snapshots over the last 30 days.
Rank by placement
Rank in Scientific research
In its category
How this plugin compares with others in the same category. Positions are counted within the category, not across the whole directory.
Scientific research33rd of 34 by placement
| # | Plugin | Position | 7d |
|---|---|---|---|
| 30 | #6414 | 19places up | |
| 31 | #6529 | 41places up | |
| 32 | #6697 | — | |
| 33 | #7850 | — | |
| 34 | #8325 | — |
Example prompts
- “Embed my dataset with a Helical model.”
- “Fine-tune a model on my labelled dataset.”
Skills
check-credits
Read the user's Helical credit balance, tell them what a run will cost and when they are actually charged, and explain a run refused for insufficient credit. Use when the user asks what their balance is, whether they can afford a run, what an embedding or fine-tune will cost, or why a run would not start.
compute-embeddings
Compute cell embeddings for a single-cell dataset with a Helical foundation model, then download the outputs. Use when the user wants embeddings, representations, or features from a dataset already in the platform catalogue. For training a model on labelled data, use fine-tune-model instead.
fine-tune-model
Fine-tune a Helical foundation model on a labelled single-cell dataset. Use when the user wants to personalize, train, or adapt a model to their own labels. For representations from an existing model without training, use compute-embeddings instead.
in-silico-perturbation
Run an in-silico gene knockout, knockdown or overexpression screen with Helical foundation models by editing the expression matrix locally, then embedding the perturbed cells on the platform. Use when the user wants to perturb genes, screen targets, or find genes whose perturbation shifts diseased cells toward a healthy state — even when no perturbation tool is available, because none is needed.
reconnect-helical
Get a Helical connection working when it is not: the plugin was installed but never connected, a tool call came back with an authorization link, or a connection that worked before is now refused. Use when the helical tools are missing from the session or the host offers only an `authenticate` tool for it; when a Helical call returns "Authorization required before this tool can run", "This request requires more information" or a link to open; when it fails with invalid_grant, invalid_request, invalid_client, invalid_token, unauthorized_client, a 401, "OAuth authorization required", "Missing Bearer token" or "Invalid JWT"; when a call fails without saying why; or when the user asks how to sign in. For a run refused over credit, use check-credits.
- Categories
- Sign-in
- On Install
- Capabilities
- Upload datasets, Compute embeddings, Fine-tune models, Download results
- Availability
- Available
- Version
- 1.0.2
- Keywords
- embeddings, fine-tuning, single-cell, bioinformatics, foundation-models, mcp
- Created
- October 4, 2026
- More info
- Sponsorship
- Promote this plugin