Background: Accumulating experimental studies 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 predicting LncRNA–disease associations based on computational models
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 the Boolean matrix for LncRNA-disease association
prediction model (HEBLDA). HEBLDA discovers the intrinsic hierarchical correlation
based on the property of the Boolean matrix from various relational sources. Then,
HEBLDA integrates 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. Besides, case studies
show that HEBLDA can accurately predict candidate disease for several LncRNAs.
Conclusion: Based on its ability to discover the more-richer correlated structure of
various data sources, we can anticipate that HEBLDA is a potential method that can
obtain more comprehensive association prediction in a broad field.