Journal
BRIEFINGS IN BIOINFORMATICS
Volume 23, Issue 1, Pages -Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bib/bbab361
Keywords
deep learning; matrix completion; conditional random field; lncRNA-disease associations
Funding
- National Natural Science Foundation of China [62162015, 61762026, 61462018, 61903248]
- Guangxi Natural Science Foundation [2017GXNSFAA198278]
- Innovation Project of GUET Graduate Education [2019YCXS056]
- GUET Excellent Graduate Thesis Program [18YJPYSS14]
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This study proposed a novel prediction method based on graph convolutional matrix completion to efficiently predict lncRNA-disease associations, which outperformed existing methods on benchmark datasets and disease case studies.
Long noncoding RNAs (lncRNAs) play important roles in various biological regulatory processes, and are closely related to the occurrence and development of diseases. Identifying lncRNA-disease associations is valuable for revealing the molecular mechanism of diseases and exploring treatment strategies. Thus, it is necessary to computationally predict lncRNA-disease associations as a complementary method for biological experiments. In this study, we proposed a novel prediction method GCRFLDA based on the graph convolutional matrix completion. GCRFLDA first constructed a graph using the available lncRNA-disease association information. Then, it constructed an encoder consisting of conditional random field and attention mechanism to learn efficient embeddings of nodes, and a decoder layer to score lncRNA-disease associations. In GCRFLDA, the Gaussian interaction profile kernels similarity and cosine similarity were fused as side information of lncRNA and disease nodes. Experimental results on four benchmark datasets show that GCRFLDA is superior to other existing methods. Moreover, we conducted case studies on four diseases and observed that 70 of 80 predicted associated lncRNAs were confirmed by the literature.
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