Issue 51, 2017

A network similarity integration method for predicting microRNA-disease associations

Abstract

Increasing evidence has indicated that microRNAs (miRNAs) regulate gene expression at the post-transcriptional level. Aberrant miRNA expression has been associated with many types of human disease, including cancers. Their associations can be used to understand the pathogenesis of diseases. However, using experimental methods to identify the associations between diseases and miRNAs is time consuming and costly. Computational methods could find the most promising miRNA-disease associations in a short time, thereby significantly reducing experimental time and cost. This paper presents a network similarity integration method (NSIM) for predicting potential miRNA-disease associations, considering that diseases associated with highly related miRNAs are more similar (and vice versa). The NSIM is based on 5425 experimentally verified human miRNA-disease associations, which consist of 495 miRNAs and 381 diseases. The NSIM integrates the disease similarity network, miRNA similarity network, and known miRNA-disease association network on the basis of cousin similarity to predict novel miRNA-disease associations. We evaluate the NSIM using leave-one-out cross validation. The area under the curve of the method is 0.9475, indicating its outstanding performance. Case studies on prostate, breast, and colon neoplasms further proved the outstanding performance of the NSIM to predict not only disease-related miRNAs but also isolated diseases (diseases without any related miRNAs).

Graphical abstract: A network similarity integration method for predicting microRNA-disease associations

Supplementary files

Article information

Article type
Paper
Submitted
11 May 2017
Accepted
12 Jun 2017
First published
23 Jun 2017
This article is Open Access
Creative Commons BY-NC license

RSC Adv., 2017,7, 32216-32224

A network similarity integration method for predicting microRNA-disease associations

X. Li, Y. Lin and C. Gu, RSC Adv., 2017, 7, 32216 DOI: 10.1039/C7RA05348G

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. You can use material from this article in other publications, without requesting further permission from the RSC, provided that the correct acknowledgement is given and it is not used for commercial purposes.

To request permission to reproduce material from this article in a commercial publication, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party commercial publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements