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.
Highlights
By integrating the association probability obtained from matrix decomposition through sparse learning method, the miRNA-disease associations and integrated disease (miRNA) functional similarity, the disease semantic similarity, and the Gaussian interaction profile kernel similarity for diseases and miRNAs into a heterogeneous network (Chen et al, 2018c), a computational model of matrix decomposition and heterogeneous graph inference is introduced to predict the miRNA-disease association
Inspired by Shao et al (2019), we developed a novel method for miRNA-disease association identification called two-objective evolutionary programming genetic algorithm (MDA-TOEPGA) from the viewpoint of functional module based on network topological properties, i.e., size and the average shortest path (ASP)
To assess the results of the identified miRNA functional modules, 326 diseases containing associated miRNAs are used as a benchmark dataset
Summary
MicroRNAs (miRNAs) are small non-coding RNAs of approximately 22 nucleotides in length that play critical roles in various types of biological processes and complex diseases, including cancer (Fujii, 2018; He et al, 2017). Chen et al (2020b) proposed a neoteric Bayesian model combining kernel-based nonlinear dimensionality reduction, matrix factorization and binary classification. By integrating the association probability obtained from matrix decomposition through sparse learning method, the miRNA functional similarity, the disease semantic similarity, and the Gaussian interaction profile kernel similarity for diseases and miRNAs into a heterogeneous network (Chen et al, 2018c), a computational model of matrix decomposition and heterogeneous graph inference is introduced to predict the miRNA-disease association. In Chen et al (2019b), a computational framework integrating ensemble learning and dimensionality reduction was developed to infer potential miRNA-disease association, the performance evaluation and case studies demonstrated the effectiveness of the proposed method. A computational model of laplacian regularized sparse subspace learning for miRNA-disease association prediction from another viewpoint (Chen et al, 2017a).
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