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10 20, 2022;12 (1): 17519.

Heterogeneous graph neural network for lncRNA-disease association prediction.

Hong Shi, Xiaomeng Zhang, Lin Tang, Lin Liu
Author Information
  1. Hong Shi: School of Information, Yunan Normal University, Kunming, 650092, China.
  2. Xiaomeng Zhang: School of Information, Yunan Normal University, Kunming, 650092, China.
  3. Lin Tang: Key Laboratory of Educational Informatization for Nationalities Ministry of Education, Yunnan Normal University, Kunming, 650092, China.
  4. Lin Liu: School of Information, Yunan Normal University, Kunming, 650092, China. liulinrachel@163.com.
PMID: 36266433 DOI: 10.1038/s41598-022-22447-y

Abstract

Identifying lncRNA-disease associations is conducive to the diagnosis, treatment and prevention of diseases. Due to the expensive and time-consuming methods verified by biological experiments, prediction methods based on computational models have gradually become an important means of lncRNA-disease associations discovery. However, existing methods still have challenges to make full use of network topology information to identify potential associations between lncRNA and disease in multi-source data. In this study, we propose a novel method called HGNNLDA for lncRNA-disease association prediction. First, HGNNLDA constructs a heterogeneous network composed of lncRNA similarity network, lncRNA-disease association network and lncRNA-miRNA association network; Then, on this heterogeneous network, various types of strong correlation neighbors with fixed size are sampled for each node by restart random walk; Next, the embedding information of lncRNA and disease in each lncRNA-disease association pair is obtained by the method of type-based neighbor aggregation and all types combination though heterogeneous graph neural network, in which attention mechanism is introduced considering that different types of neighbors will make different contributions to the prediction of lncRNA-disease association. As a result, the area under the receiver operating characteristic curve (AUC) and the area under the precision-recall curve (AUPR) under fivefold cross-validation (5FCV) are 0.9786 and 0.8891, respectively. Compared with five state-of-art prediction models, HGNNLDA has better prediction performance. In addition, in two types of case studies, it is further verified that our method can effectively predict the potential lncRNA-disease associations, and have ability to predict new diseases without any known lncRNAs.

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MeSH Term

RNA, Long Noncoding
Computational Biology
Algorithms
Neural Networks, Computer
MicroRNAs

Chemicals

RNA, Long Noncoding
MicroRNAs
Journal Article Research Support, Non-U.S. Gov't
Article has an altmetric score of 2

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