A Novel Neighborhood-Based Computational Model for Potential MiRNA-Disease Association Prediction

Computational and Mathematical Methods in Medicine
Yang LiuLei Wang

Abstract

In recent years, more and more studies have shown that miRNAs can affect a variety of biological processes. It is important for disease prevention, treatment, diagnosis, and prognosis to study the relationships between human diseases and miRNAs. However, traditional experimental methods are time-consuming and labour-intensive. Hence, in this paper, a novel neighborhood-based computational model called NBMDA is proposed for predicting potential miRNA-disease associations. Due to the fact that known miRNA-disease associations are very rare and many diseases (or miRNAs) are associated with only one or a few miRNAs (or diseases), in NBMDA, the K-nearest neighbor (KNN) method is utilized as a recommendation algorithm based on known miRNA-disease associations, miRNA functional similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity for miRNAs and diseases to improve its prediction accuracy. And simulation results demonstrate that NBMDA can effectively infer miRNA-disease associations with higher accuracy compared with previous state-of-the-art methods. Moreover, independent case studies of esophageal neoplasms, breast neoplasms and colon neoplasms are further implemented, and as a result, there are...Continue Reading

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Citations

May 25, 2021·Frontiers in Molecular Biosciences·Sandra BrasilVanessa Dos Reis Ferreira

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Methods Mentioned

BETA
PCR
MDA
SMA

Software Mentioned

miR2Disease
SMA
NBCLDA
RLSMDA
SDA
NBMDA
SDMMDA
WBSMDA
KATZMDA
dbDEMC

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