Knowledge graph embedding for profiling the interaction between transcription factors and their target genes

Interactions between transcription factor and target gene form the main part of gene regulation network in human, which are still complicating factors in biological research. Specifically, for nearly half of those interactions recorded in established database, their interaction types are yet to be confirmed. Although several computational methods exist to predict gene interactions and their type, there is still no method available to predict them solely based on topology information. To this end, we proposed here a graph-based prediction model called KGE-TGI and trained in a multi-task learning manner on a knowledge graph that we specially constructed for this problem. The KGE-TGI model relies on topology information rather than being driven by gene expression data. In this paper, we formulate the task of predicting interaction types of transcript factor and target genes as a multi-label classification problem for link types on a heterogeneous graph, coupled with solving another link prediction problem that is inherently related. We constructed a ground truth dataset as benchmark and evaluated the proposed method on it. As a result of the 5-fold cross experiments, the proposed method achieved average AUC values of 0.9654 and 0.9339 in the tasks of link prediction and link type classification, respectively. In addition, the results of a series of comparison experiments also prove that the introduction of knowledge information significantly benefits to the prediction and that our methodology achieve state-of-the-art performance in this problem.Author summary: The interaction between transcription factors (TFs) and their target genes is a fundamental aspect of transcriptional regulation research, but the number of these interactions that can be studied is currently limited by biological techniques. And the computational methods relevant to the prediction of transcriptional regulation relationships are still not accurate enough and are unable to predict the type of transcriptional regulation interactions. This study presents a multi-task approach namely KGE-TGI for predicting the existence of the interactions between transcription factors and their target genes and the type of them on a knowledge graph. To evaluate our method, we constructed a ground truth dataset and conducted 5-fold cross experiments. The results showed that our method achieved average AUC values of 0.9654 and 0.9339 for link prediction and link type classification, respectively. Comparison experiments also demonstrated that incorporating knowledge information significantly improved performance and our method achieved state-of-the-art results for this problem.