Background: Accumulating experimental studies have manifested that long-non-coding
RNAs (lncRNAs) play an important part in various biological process. It has been shown that their
alterations and dysregulations are closely related to many critical complex diseases.
Objective: It is of great importance to develop effective computational models for predicting
potential lncRNA-disease associations.
Method: Based on the hypothesis that there would be potential associations between a lncRNA
and a disease if both of them have associations with the same group of microRNAs, and similar
diseases tend to be in close association with functionally similar lncRNAs. A novel method for
calculating similarities of both lncRNAs and diseases is proposed, and then a novel prediction
model LDLMD for inferring potential lncRNA-disease associations is proposed.
Results: LDLMD can achieve an AUC of 0.8925 in the Leave-One-Out Cross Validation
(LOOCV), which demonstrated that the newly proposed model LDLMD significantly outperforms
previous state-of-the-art methods and could be a great addition to the biomedical research field.
Conclusion: Here, we present a new method for predicting lncRNA-disease associations,
moreover, the method of our present decrease the time and cost of biological experiments.