ibgnn
Use this model doc whenever the user wants to run IBGNN (Interpretable Brain Graph Neural Network) for fMRI phenotype prediction. IBGNN is a PyG-based GNN with a learnable MLP message function over [x_i, x_j, edge_attr], designed for connectome-based brain disorder analysis with post-hoc edge-mask explainer support.
npx skills add BioTender-max/awesome-bio-agent-skills --skill ibgnn --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.
# IBGNN Model Doc ## Overview IBGNN (Interpretable Brain Graph Neural Network) 是面向脑连接组分析的可解释 GNN。核心组件是 MPConv —— 在 GCN 归一化基础上，将消息计算从简单加权聚合改为 `MLP([x_i, x_j, edge_attr])` 学习消息函数。模型与 post-hoc 边遮罩 explainer 配合使用，可提取对预测重要的边子图。 - Paper: Cui et al., 2022, "Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysis"，MICCAI - Official code: https://github.com/HennyJie/IBGNN - NeuroClaw reimplementation: `models/ibgnn/`（移除 explainer 用的 edge_flag 机制，仅保留 encoder） - Primary input: PyG Data graph（与 BrainGNN 共享数据格式） - Primary output: phenotype prediction（可选 attention/重要边解释） **Research use only.** --- ## NeuroClaw 实现要点 1. **MPConv 核心**：每条边的消息 = `Linear([x_i, x_j, edge_attr])`，比 GCN 多一层非线性表达。 2. **GCN 归一化**：边权 |corr| 经过对称归一化（与 GCN 相同），再注入 self-loop。 3. **去除 edge_flag**：原版用于 explainer 屏蔽边，普通 forward 中 edge_flag 是全 1 tensor，NeuroClaw 直接砍掉以简化代码。 4. **正边权约束**：`edge_attr.abs()` 后传入，与 BrainGNN 同样做法（softmax 类操作需要非负）。 5. **任务统一接口**：classification (`nclass=N`) 与 regression (`nclass=1, task='regression'`) 一套代码。 6. **PyG 2.7 兼容**：`torch_scatter.scatter_add` 已被 `torch_geometric.utils.scatter(reduce='sum')` 替代。 7. **数据复用**：直接复用 BrainGNN 的 `NeuroClawFCDataset`，无需额外预处理。 --- ## Quick Start (
- Overview
- NeuroClaw 实现要点
- Quick Start (NeuroClaw 内部)
- 前置条件
- 训练（分类，单 fold 冒烟测试）
- 训练（回归，HCP age）
- 核心文件
- 模型架构
- 关键训练参数
- 调试经验与注意事项
- NeuroClaw 委托规则
- Reference
python skills/ibgnn/scripts/train_reference.py \
What does the ibgnn skill do?
Use this model doc whenever the user wants to run IBGNN (Interpretable Brain Graph Neural Network) for fMRI phenotype prediction. IBGNN is a PyG-based GNN with a learnable MLP message function over [x_i, x_j, edge_attr], designed for connectome-based brain disorder analysis with post-hoc edge-mask explainer support.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill ibgnn --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.