通过强制序列嵌入的组合结构，提升蛋白质网络的功能解析能力。
Event Embedding of Protein Networks : Compositional Learning of Biological Function
- 使用加性嵌入模型Event2Vec学习蛋白质交互网络表示
- 组合结构使通路一致性提升30.2倍，功能相似度达0.966
- 适合需要推理生物功能关系的研究者使用
本文研究在蛋白质-蛋白质相互作用网络中，强制序列嵌入具备严格组合结构是否能带来有意义的几何组织。采用事件嵌入模型Event2Vec，在人类STRING互作网络的随机游走数据上训练64维表示，并与基于Word2Vec的DeepWalk基线进行对比。结果表明，组合结构显著提升了通路一致性（30.2倍于随机水平，对比基线2.9倍）、功能类比准确率（均相似度0.966，对比基线0.650）以及通路层级组织能力，而几何特性如范数-度数反相关性则与非组合基线相当或更优。说明强制组合性特别有助于生物网络中的关系与组合推理任务。
原文摘要 · Abstract (English)
In this work, we study whether enforcing strict compositional structure in sequence embeddings yields meaningful geometric organization when applied to protein-protein interaction networks. Using Event2Vec, an additive sequence embedding model, we train 64-dimensional representations on random walks from the human STRING interactome, and compare against a DeepWalk baseline based on Word2Vec, trained on the same walks. We find that compositional structure substantially improves pathway coherence (30.2$\times$ vs 2.9$\times$ above random), functional analogy accuracy (mean similarity 0.966 vs 0.650), and hierarchical pathway organization, while geometric properties such as norm--degree anticorrelation are shared with or exceeded by the non-compositional baseline. These results indicate that enforced compositionality specifically benefits relational and compositional reasoning tasks in biological networks.
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