论文标题
一种知识图表示学习方法，以预测新型激酶 - 基底相互作用
A knowledge graph representation learning approach to predict novel kinase-substrate interactions
论文作者
论文摘要
人蛋白质组包含一个庞大的相互作用激酶和底物网络。即使某些激酶被证明是治疗靶标非常有用的，但大多数仍在研究中。在这项工作中，我们提出了一种新颖的知识图表示学习方法，以预测研究研究研究的新型相互作用伙伴。我们的方法使用通过整合来自IPTMNET，蛋白质本体论，基因本体论和BIOKG的数据构建的磷蛋白学知识图。通过在三元组上进行定向的随机步行，与修改后的Skipgram或CBOW模型一起进行定向的随机步行，可以从此知识图中的激酶和底物表示。然后，这些表示形式被用作监督分类模型的输入，以预测研究研究研究的新型相互作用。我们还对预测的相互作用和对磷酸蛋白质学知识图的消融研究进行了预测性分析，以深入了解研究研究的激酶的生物学。
The human proteome contains a vast network of interacting kinases and substrates. Even though some kinases have proven to be immensely useful as therapeutic targets, a majority are still understudied. In this work, we present a novel knowledge graph representation learning approach to predict novel interaction partners for understudied kinases. Our approach uses a phosphoproteomic knowledge graph constructed by integrating data from iPTMnet, Protein Ontology, Gene Ontology and BioKG. The representation of kinases and substrates in this knowledge graph are learned by performing directed random walks on triples coupled with a modified SkipGram or CBOW model. These representations are then used as an input to a supervised classification model to predict novel interactions for understudied kinases. We also present a post-predictive analysis of the predicted interactions and an ablation study of the phosphoproteomic knowledge graph to gain an insight into the biology of the understudied kinases.
