Open Access

ARTICLE

MDA-TOEPGA: A novel method to identify miRNA-disease association based on two-objective evolutionary programming genetic algorithm

BUWEN CAO^1,*, JIAWEI LUO^2,*, SAINAN XIAO^1,2, XIANGJUN ZHOU¹

1 College of Information and Electronic Engineering, Hunan City University, Yiyang, 413000, China
2 College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, China

* Corresponding Authors: Buwen Cao, ; Jiawei Luo,

(This article belongs to the Special Issue: Computational Models in Non-Coding RNA and Human Disease)

BIOCELL 2022, 46(8), 1925-1933. https://doi.org/10.32604/biocell.2022.019613

Received 01 October 2021; Accepted 29 November 2021; Issue published 22 April 2022

Abstract

The association between miRNA and disease has attracted more and more attention. Until now, existing methods for identifying miRNA related disease mainly rely on top-ranked association model, which may not provide a full landscape of association between miRNA and disease. Hence there is strong need of new computational method to identify the associations from miRNA group view. In this paper, we proposed a framework, MDA-TOEPGA, to identify miRNAdisease association based on two-objective evolutionary programming genetic algorithm, which identifies latent miRNAdisease associations from the view of functional module. To understand the miRNA functional module in diseases, the case study is presented. We have been compared MDA-TOEPGA with several state-of-the-art functional module algorithm. Experimental results showed that our method cannot only outperform classical algorithms, such as K-means, IK-means, MCODE, HC-PIN, and ClusterONE, but can also achieve an ideal overall performance in terms of a composite score consisting of f1, Sensitivity, and Accuracy. Altogether, our study showed that MDA-TOEPGA is a promising method to investigate miRNA-disease association from the landscapes of functional module.

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Keywords

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