A Novel Model for Predicting LncRNA-disease Associations based on the LncRNA-MiRNA-Disease Interactive Network

Author(s): Lei Wang, Zhanwei Xuan*, Shunxian Zhou, Linai Kuang, Tingrui Pei.

Journal Name: Current Bioinformatics

Volume 14 , Issue 3 , 2019

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Graphical Abstract:


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.

Keywords: Similarity, computing model, prediction, lncRNA-disease associations, LncRNA-MiRNA-disease interactive network.

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Article Details

Year: 2019
Page: [269 - 278]
Pages: 10
DOI: 10.2174/1574893613666180703105258
Price: $58

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