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Inferring MicroRNA-Disease Associations Based on the Identification of a Functional Module
Buwen Cao1,2, Shuguang Deng1, Hua Qin1
1College of Information and Electronic Engineering, Hunan City University, Yiyang, China.
Abstract:
Inferring potential associations between microRNAs (miRNAs) and human diseases can help people understand the pathogenesis of complex human diseases. Several computational approaches have been presented to discover novel miRNA-disease associations based on a top-ranked association model. However, some top-ranked miRNAs are not easily used to reveal the association between miRNAs and diseases. This study aims to infer miRNA-disease relationship by identifying a functional module. We first construct a miRNA functional similarity network derived from a disease similarity network and a known miRNA-disease relationship network. We then present an improved K-means (i.e., IK-means) algorithm to detect miRNA functional modules and used 243 diseases to validate the performance of our proposed method. Experimental results indicate that the performance of IK-means is better compared with classical K-means algorithms. Case studies on some functional modules further demonstrate the applicability of IK-means in the identification of new miRNA-disease associations.
Insights
This study introduces an improved K-means algorithm (IK-means) to identify functional modules for inferring microRNA (miRNA)-disease associations. IK-means enhances the discovery of novel miRNA-disease relationships, aiding in understanding disease pathogenesis.
Area of Science:
- Bioinformatics
- Genomics
- Computational Biology
Background:
- Understanding microRNA (miRNA)-disease associations is crucial for elucidating complex human disease pathogenesis.
- Existing computational methods for discovering miRNA-disease associations often rely on top-ranked models, which may not always be easily interpretable.
- There is a need for improved computational approaches to identify robust miRNA-disease relationships.
Purpose of the Study:
- To infer miRNA-disease relationships by identifying functional modules.
- To develop and validate an improved K-means (IK-means) algorithm for detecting miRNA functional modules.
- To demonstrate the applicability of the proposed method in identifying novel miRNA-disease associations.
Main Methods:
- Constructed a miRNA functional similarity network integrating disease similarity and known miRNA-disease association networks.
- Developed an improved K-means (IK-means) algorithm for miRNA functional module detection.
- Validated the IK-means algorithm's performance using data from 243 diseases.
Main Results:
- The IK-means algorithm demonstrated superior performance compared to classical K-means algorithms in detecting miRNA functional modules.
- Case studies confirmed the practical utility of IK-means in identifying previously unknown miRNA-disease associations.
- The functional module identification approach effectively infers miRNA-disease relationships.
Conclusions:
- The proposed IK-means algorithm is an effective tool for identifying miRNA functional modules.
- This method enhances the inference of miRNA-disease associations, contributing to a better understanding of disease mechanisms.
- The approach holds promise for discovering novel biomarkers and therapeutic targets related to miRNAs and diseases.
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