spacenet
Use this model doc whenever the user wants to perform disease classification with SpaceNet. This is a non-deep-learning supervised route focused on voxel-wise neuroimaging-based case-control prediction with sparse and interpretable weight maps.
npx skills add BioTender-max/awesome-bio-agent-skills --skill spacenet --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# SpaceNet Model Doc ## Overview SpaceNet is a classical non-deep-learning method for neuroimaging-based disease classification. - Model family: non-deep-learning supervised classification method - Typical objectives: - classify patient vs control groups from voxel-wise neuroimaging maps - build sparse discriminative models in aligned image space - export predictive scores, evaluation metrics, and interpretable weight maps - Primary input: aligned subject images, labels, optional covariates, optional mask - Primary output: class predictions, decision scores, cross-validation metrics, coefficient maps In NeuroClaw, this document is model-level guidance for SpaceNet-based disease classification workflows rather than deep learning phenotype prediction. Upstream preparation should usually be delegated to: - `fmri-skill` for fMRI preprocessing and voxel-wise feature preparation - `smri-skill` for structural feature extraction when disease classification uses sMRI - `nilearn-tool` for concrete SpaceNet fitting and coefficient map export **Research use only.** --- ## Quick Start ### 1) Prepare disease classification inputs Expected inputs: - subject-level labels such as patient / control
- Overview
- Quick Start
- 1) Prepare disease classification inputs
- 2) SpaceNet route
- Input / Output Contract
- Required inputs
- Optional inputs
- Produced outputs
- Recommended Delegation
- When to Use SpaceNet
- Limitations and Notes
- Reference
delegated through claw-shell after voxel maps are prepared python skills/nilearn-tool/scripts/spacenet_classifier_reference.py \
What does the spacenet skill do?
Use this model doc whenever the user wants to perform disease classification with SpaceNet. This is a non-deep-learning supervised route focused on voxel-wise neuroimaging-based case-control prediction with sparse and interpretable weight maps.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill spacenet --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.