论文标题
学习生化网络的图形表示及其在酶促链接预测中的应用
Learning graph representations of biochemical networks and its application to enzymatic link prediction
论文作者
论文摘要
分子之间的酶活性的完整表征仍然不完整，阻碍了生物学工程和限制生物学发现。我们在这项工作中开发了一种技术，即酶线链接预测（ELP），以预测两个分子之间酶促转化的可能性。 ELP模型酶促反应在KEGG数据库中分类为图。 ELP对使用图嵌入来学习分子表示不仅捕获分子和酶促属性，而且还图形连接性的分子表示方面具有创新性。 我们探索两个转导性（训练图中包含的测试节点）和电感（测试节点而不是训练图的一部分）学习模型。我们表明，使用图形连接和节点属性学习节点嵌入时，ELP可以实现高AUC。此外，我们表明，用于预测酶促链接的图嵌入将链接预测提高了24％，而基于指纹相似性的方法。为了强调在生化网络中嵌入图的重要性，我们说明了图嵌入方式还可以指导可视化。 可以通过https://github.com/hassounlab/elp获得代码和数据集。
The complete characterization of enzymatic activities between molecules remains incomplete, hindering biological engineering and limiting biological discovery. We develop in this work a technique, Enzymatic Link Prediction (ELP), for predicting the likelihood of an enzymatic transformation between two molecules. ELP models enzymatic reactions catalogued in the KEGG database as a graph. ELP is innovative over prior works in using graph embedding to learn molecular representations that capture not only molecular and enzymatic attributes but also graph connectivity. We explore both transductive (test nodes included in the training graph) and inductive (test nodes not part of the training graph) learning models. We show that ELP achieves high AUC when learning node embeddings using both graph connectivity and node attributes. Further, we show that graph embedding for predicting enzymatic links improves link prediction by 24% over fingerprint-similarity-based approaches. To emphasize the importance of graph embedding in the context of biochemical networks, we illustrate how graph embedding can also guide visualization. The code and datasets are available through https://github.com/HassounLab/ELP.
