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

• Published in: PLoS computational biology Vol. 19; no. 6; p. e1011207
• Main Authors: Wu, Yang-Han, Huang, Yu-An, Li, Jian-Qiang, You, Zhu-Hong, Hu, Peng-Wei, Hu, Lun, Leung, Victor C. M., Du, Zhi-Hua
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.06.2023; Public Library of Science (PLoS)
• Subjects: Analysis; Benchmarking; Binding sites; Biological research; Biology and Life Sciences; Classification; Computer and Information Sciences; Databases, Factual; Datasets; DNA binding proteins; Embedding; Epistasis, Genetic; Experiments; Gene expression; Gene regulation; Genes; Genetic aspects; Graphic methods; Humans; Identification and classification; Knowledge; Knowledge representation; Laboratories; Methods; Neural networks; Pattern Recognition, Automated; Prediction models; R&D; Research & development; Research and Analysis Methods; RNA polymerase; Topology; Transcription factors; Transcription Factors - genetics; United States; China
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1011207

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.