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Curr Mol Med. 2019 Nov 18. doi: 10.2174/1566524019666191119104212. [Epub ahead of print]

Hierarchical extension based on Boolean matrix for LncRNA-disease association prediction.

Author information

1
Key Laboratory of Educational Informatization for Nationalities Ministry of Education, Yunnan Normal University, Kunming, Yunnan, China.
2
School of Software, Yunnan University, Kunming, Yunnan, China.
3
School of Information, Yunnan Normal University, Kunming, Yunnan, China.

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:

Associated matrix.; Association prediction; Boolean matrix; Hierarchical extension; LncRNA; disease

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