Hierarchical Extension Based on the Boolean Matrix for LncRNADisease Association Prediction

(E-pub Ahead of Print)

Author(s): Lin Tang, Yu Liang, Xin Jin, Lin Liu, Wei Zhou*.

Journal Name: Current Molecular Medicine

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Abstract:

Background:Accumulating experimental studies have demonstrated that long non-coding RNAs (LncRNAs) play crucial roles in the occurrence and development progress of various complex human diseases. Nonetheless, only a small portion of LncRNA–disease associations have been experimentally verified at present. Automatically predict LncRNA–disease associations based on computational model can save the huge cost of wet-lab experiments.

Result:To develop effective computational models to integrate various heterogeneous biological data for the identification of potential disease-LncRNA, we propose a hierarchical extension based on Boolean matrix for LncRNA-disease association prediction model (HEBLDA). HEBLDA discover the intrinsic hierarchical correlation based on the property of Boolean matrix from various relational sources. Then, HEBLDA integrate these hierarchical associated matrices by fusion weights. Finally, HEBLDA uses the hierarchical associated matrix to reconstruct the LncRNA–disease association matrix by hierarchical extending. HEBLDA is able to work for potential diseases or LncRNA without known association data. In 5-fold cross validation experiments, HEBLDA obtained an area under the receiver operating characteristic curve (AUC) of 0.8913, improving previous classical methods. Beside, case studies show that HEBLDA can accurately predict candidate disease for several LncRNAs.

Conclusion:Based on its ability of discovering more-richer correlated structure of various data sources, we can anticipate that HEBLDA is a potential method can obtain more comprehensive association prediction in a broad field.

Keywords: LncRNA, disease, Association prediction, Boolean matrix, Hierarchical extension, Associated matrix.

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Article Details

(E-pub Ahead of Print)
DOI: 10.2174/1566524019666191119104212
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