Semantic meta-path enhanced global and local topology learning for lncRNA-disease association prediction

Since abnormal expression of long non-coding RNAs (lncRNAs) is associated with various human diseases, identifying disease-related lncRNAs helps reveal the pathogenesis of diseases. Existing methods for lncRNA-disease association prediction mainly focus on multi-sourced data related to lncRNAs and d...

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Veröffentlicht in:	IEEE/ACM Transactions on Computational Biology and Bioinformatics Jg. 20; H. 2; S. 1 - 11
Hauptverfasser:	Xuan, Ping, Zhao, Yue, Cui, Hui, Zhan, Linyun, Jin, Qiangguo, Zhang, Tiangang, Nakaguchi, Toshiya
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.03.2023 Institute of Electrical and Electronics Engineers (IEEE) The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Ablation Bioinformatics Convolution Decoding Disease Diseases Embedding Graph convolutional autoencoder with attention Humans Learning LncRNA-disease association prediction LncRNA-disease-miRNA heterogeneous graph Meta-path based semantic learning MicroRNAs MicroRNAs - genetics miRNA Nodes Non-coding RNA Pathogenesis Predictions Predictive models RNA, Long Noncoding RNA, Long Noncoding - genetics Semantics Topology
ISSN:	1545-5963, 1557-9964, 2374-0043, 1557-9964
Online-Zugang:	Volltext
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