GraphTGI: an attention-based graph embedding model for predicting TF-target gene interactions. Issue 3 (30th April 2022)

• Record Type: Journal Article
• Authors: Du, Zhi-Hua; Wu, Yang-Han; Huang, Yu-An; Chen, Jie; Pan, Gui-Qing; Hu, Lun; You, Zhu-Hong; Li, Jian-Qiang
• Is Part Of: Briefings in bioinformatics. Volume 23:Issue 3(2022)
• Publication Date: 2022-04-30
• Subjects: transcriptional regulatory network -- transcription factor -- graph neural network -- chemical similarity -- graph auto-encoder; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbac148
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Motivation: Interaction between transcription factor (TF) and its target genes establishes the knowledge foundation for biological researches in transcriptional regulation, the number of which is, however, still limited by biological techniques. Existing computational methods relevant to the prediction of TF-target interactions are mostly proposed for predicting binding sites, rather than directly predicting the interactions. To this end, we propose here a graph attention-based autoencoder model to predict TF-target gene interactions using the information of the known TF-target gene interaction network combined with two sequential and chemical gene characters, considering that the unobserved interactions between transcription factors and target genes can be predicted by learning the pattern of the known ones. To the best of our knowledge, the proposed model is the first attempt to solve this problem by learning patterns from the known TF-target gene interaction network. Results: In this paper, we formulate the prediction task of TF-target gene interactions as a link prediction problem on a complex knowledge graph and propose a deep learning model called GraphTGI, which is composed of a graph attention-based encoder and a bilinear decoder. We evaluated the prediction performance of the proposed method on a real dataset, and the experimental results show that the proposed model yields outstanding performance with an average AUC value of 0.8864 +/- 0.0057 in the 5-fold cross-validation. It is anticipated that the GraphTGI model can effectively and efficiently predict TF-target gene interactions on a large scale. Availability: Python code and the datasets used in our studies are made available at https://github.com/YanghanWu/GraphTGI