Abstract
Long non-coding RNA (lncRNA) plays an important role in many biological processes. A large number of studies have shown that predicting the associations between lncRNAs and diseases may uncover the causation of various diseases. However, experimental determination of the lncRNA-disease associations is expensive and time-consuming. Many methods are emerging, but it is still a challenge to predict the associations between lncRNAs and diseases more accurate. More and more evidences suggest that lncRNA has interaction with miRNA, which is highly related to the occurrence of cancer, gene regulation, and cell metabolism. Therefore, in this paper, we design a new method based on the interactions of lncRNAs, diseases and miRNAs. We represent the lncRNA-disease-miRNA triplet as a tensor, innovatively, and predict the potential associations between lncRNAs and diseases via tensor decomposition. First, we build lncRNAs similarity matrix by integrating semantic information and functional interactions. Second, we collect the pairwise associations between lncRNAs, diseases, and miRNAs, respectively, and integrate them into a three-dimensional association tensor. Third, we utilize the lncRNAs similarity matrix and diseases similarity matrix as auxiliary information to perform tensor decomposition with the association tensor to obtain factor matrices of lncRNAs, diseases and miRNAs, respectively. Finally, we use the factor matrices to reconstruct the association tensor for new prediction of triplet associations. To evaluate the performance of TDLDA, we compare our method with other methods and find it more superior.
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This work was supported by Natural Science Foundation of China (Grant. No. 61972141).
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Lu, X., Yuan, Y., Chen, G., Li, J., Jiang, K. (2021). Predicting LncRNA-Disease Associations Based on Tensor Decomposition Method. In: Huang, DS., Jo, KH., Li, J., Gribova, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science(), vol 12838. Springer, Cham. https://doi.org/10.1007/978-3-030-84532-2_26
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DOI: https://doi.org/10.1007/978-3-030-84532-2_26
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