对比图与非图方法，发现注意力型图神经网络在蛔虫神经元分类中更优。
A Benchmark Analysis of Graph and Non-Graph Methods for Caenorhabditis Elegans Neuron Classification
- 用空间、连接和活动特征构建神经元分类模型
- 基于空间和连接特征，注意力型图网络准确率显著更高
- 适合对神经网络结构建模感兴趣的科研人员
本研究建立了线虫（Caenorhabditis elegans）神经元分类的基准，对比了四种图方法（GCN、GraphSAGE、GAT、GraphTransformer）与四种非图方法（Logistic Regression、MLP、LOLCAT、NeuPRINT）。基于功能连接组，利用空间、连接和神经活动特征对感觉神经元、中间神经元和运动神经元进行分类。结果显示，基于注意力机制的图神经网络在空间和连接特征上显著优于基线模型；而神经活动特征表现较差，可能因底层数据时间分辨率低所致。该基准验证了图神经网络的有效性，并指出空间和连接特征是关键预测因子。代码已开源：https://github.com/JingqiLuu/neuronclf-gnn-benchmark。
原文摘要 · Abstract (English)
This study establishes a benchmark for Caenorhabditis elegans neuron classification, comparing four graph methods (GCN, GraphSAGE, GAT, GraphTransformer) against four non-graph methods (Logistic Regression, MLP, LOLCAT, NeuPRINT). Using the functional connectome, we classified Sensory, Interneuron, and Motor neurons based on Spatial, Connection, and Neuronal Activity features. Results show that attention-based GNNs significantly outperform baselines on the Spatial and Connection features. The Neuronal Activity features yielded poor performance, likely due to the low temporal resolution of the underlying neuronal activity data. Our benchmark validates the use of GNNs and highlights that Spatial and Connection features are key predictors for Caenorhabditis elegans neuron classes. Code is available at: https://github.com/JingqiLuu/neuronclf-gnn-benchmark.
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