fm_app
Use this model doc whenever the user wants to run FM-APP for phenotype prediction using fMRI ROI features and optional sMRI features. This document provides model-level usage and delegates preprocessing to fmri-skill and smri-skill.
npx skills add BioTender-max/awesome-bio-agent-skills --skill fm_app --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.
# FM-APP Model Doc ## Overview FM-APP is a multi-stage framework for phenotype prediction via fMRI to sMRI knowledge transfer. - Paper: He Z, Li W, Liu Y, et al. FM-APP, IEEE TMI, 2024, 44(10): 4010-4022 - Official code: https://github.com/ZhibinHe/FM-APP - Primary input: fMRI ROI connectivity features - Additional input: sMRI ROI structural features (required in Stage 2) - Primary output: multi-phenotype prediction and zero-shot phenotype reconstruction In NeuroClaw, this is model-level guidance. Upstream preparation should be delegated to: - `fmri-skill` for fMRI preprocessing and ROI extraction - `smri-skill` for structural ROI feature extraction - `hcpya-skill` if HCP Young Adult download/orchestration is needed **Research use only.** --- ## Quick Start (From git clone) ### 1) Clone repository ```bash git clone https://github.com/ZhibinHe/FM-APP.git cd FM-APP ``` ### 2) Create environment and install dependencies ```bash python -m venv .venv source .venv/bin/activate pip install -r requirements.txt ``` If using GPU, install CUDA-compatible PyTorch and graph-related packages first. ### 3) Prepare required data Before training, ensure these are ready: - fMRI ROI/connectivity feat
- Overview
- Quick Start (From git clone)
- 1) Clone repository
- 2) Create environment and install dependencies
- 3) Prepare required data
- 4) Run staged pipeline
- Pipeline Definition
- Stage Inputs and Outputs
- Stage 0 (Data Preparation)
- Stage 1 (fMRI Training)
- Stage 2 (fMRI-sMRI Alignment)
- Stage 3 (Decoder and Zero-shot)
- Typical Configuration Notes
- Recommended Directory Layout
git clone https://github.com/ZhibinHe/FM-APP.git cd FM-APP python -m venv .venv source .venv/bin/activate pip install -r requirements.txt Stage 0: data preparation (example scripts) python 00-create-folder_hcp_4fmri.py python 01-fetch_data_hcp_4fmri.py python 02-process_data_hcp_4fmri.py Stage 1: fMRI model training
What does the fm_app skill do?
Use this model doc whenever the user wants to run FM-APP for phenotype prediction using fMRI ROI features and optional sMRI features. This document provides model-level usage and delegates preprocessing to fmri-skill and smri-skill.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill fm_app --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.