Prediction of biomarker–disease associations based on graph attention network and text representation

• Published in: Briefings in bioinformatics Vol. 23; no. 5
• Main Authors: Yang, Minghao, Huang, Zhi-An, Gu, Wenhao, Han, Kun, Pan, Wenying, Yang, Xiao, Zhu, Zexuan
• Format: Journal Article
• Language: English
• Published: Oxford Oxford University Press 20.09.2022; Oxford Publishing Limited (England)
• Subjects: Availability; Biomarkers; Computer applications; Disease; Information resources; Predictions; Representations; Source code; graph attention network; text-based relation representation; lncRNA–disease associations; miRNA–disease associations; bimodal fusion network; microbe–disease associations
• ISSN: 1467-5463; 1477-4054; 1477-4054
• DOI: 10.1093/bib/bbac298

Abstract Motivation The associations between biomarkers and human diseases play a key role in understanding complex pathology and developing targeted therapies. Wet lab experiments for biomarker discovery are costly, laborious and time-consuming. Computational prediction methods can be used to greatly expedite the identification of candidate biomarkers. Results Here, we present a novel computational model named GTGenie for predicting the biomarker–disease associations based on graph and text features. In GTGenie, a graph attention network is utilized to characterize diverse similarities of biomarkers and diseases from heterogeneous information resources. Meanwhile, a pretrained BERT-based model is applied to learn the text-based representation of biomarker–disease relation from biomedical literature. The captured graph and text features are then integrated in a bimodal fusion network to model the hybrid entity representation. Finally, inductive matrix completion is adopted to infer the missing entries for reconstructing relation matrix, with which the unknown biomarker–disease associations are predicted. Experimental results on HMDD, HMDAD and LncRNADisease data sets showed that GTGenie can obtain competitive prediction performance with other state-of-the-art methods. Availability The source code of GTGenie and the test data are available at: https://github.com/Wolverinerine/GTGenie.