Prediction of potential disease-associated microRNAs using structural perturbation method
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Abstract
Motivation: The identification of disease-related microRNAs (miRNAs) is an essential but challenging task in bioinformatics research. Similarity-based link prediction methods are often used to predict potential associations between miRNAs and diseases. In these methods, all unobserved associations are ranked by their similarity scores. Higher score indicates higher probability of existence. However, most previous studies mainly focus on designing advanced methods to improve the prediction accuracy while neglect to investigate the link predictability of the networks that present the miRNAs and diseases associations. In this work, we construct a bilayer network by integrating the miRNA-disease network, the miRNA similarity network and the disease similarity network. We use structural consistency as an indicator to estimate the link predictability of the related networks. On the basis of the indicator, a derivative algorithm, called structural perturbation method (SPM), is applied to predict potential associations between miRNAs and diseases. Results: The link predictability of bilayer network is higher than that of miRNA-disease network, indicating that the prediction of potential miRNAs-diseases associations on bilayer network can achieve higher accuracy than based merely on the miRNA-disease network. A comparison between the SPM and other algorithms reveals the reliable performance of SPM which performed well in a 5-fold cross-validation. We test fifteen networks. The AUC values of SPM are higher than some well-known methods, indicating that SPM could serve as a useful computational method for improving the identification accuracy of miRNA‒disease associations. Moreover, in a case study on breast neoplasm, 80% of the top-20 predicted miRNAs have been manually confirmed by previous experimental studies. Availability and implementation: https://github.com/lecea/SPM-code.git. Supplementary information: Supplementary data are available at Bioinformatics online.
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Several researches proved that coding genes, noncoding RNAs, and proteins are related to diseases, including AD.
“Several researches proved that coding genes/noncoding RNAs/proteins were related to diseases, including AD [29,30,31,32,33,34,35,36].”
From An Efficient Classifier for Alzheimer’s Disease Genes Identification (2018) · cites this paper as [31]
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- Authors
- Zeng, X., Liu, L., Lü, L., & Zou, Q.
- Affiliations
- Xiamen University (CN)
- University of Electronic Science and Technology of China (CN)
- Tianjin University (CN)
- Corresponding author
- Lü, Linyuan
- Zou, Quan
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-02-27
- Volume
- 34
- Issue
- 14
- Pages
- 2425-2432
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2780584018
- Cited by
- 235
- References
- 31
- Citations by year
- 2025: 4 · 2024: 11 · 2023: 16 · 2022: 29 · 2021: 44 · 2020: 44 · 2019: 56 · 2018: 31
- FWCI
- 13.14
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Bioinformatics (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (61272152, 61472333, 61772441, 11622538, 61673150)
- European project funded (FP7–FET 612146)
- Zhejiang Provincial Natural Science Foundation of China (LR16A050001)
- Juan de la Cierva position (IJCI-2015-26991)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › MicroRNA in disease regulation
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer-related molecular mechanisms research
- Keywords
- microRNA-disease association prediction, link prediction, disease similarity network, structural consistency, breast cancer
- MeSH terms
- Algorithms, Breast Neoplasms, Disease Susceptibility, Female, Humans, Software, Computational Biology, MicroRNAs, Genetic Association Studies
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