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INTS-MFS: A novel method to predict microRNA-disease associations by integrating network topology similarity and microRNA function similarity

BUWEN CAO1,*, JIAWEI LUO2,*, SAINAN XIAO1, KAI ZHAO1, SHULING YANG1

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. Email: email; JIAWEI LUO. Email: email

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

BIOCELL 2022, 46(3), 837-845. https://doi.org/10.32604/biocell.2022.017538

Abstract

Identifying associations between microRNAs (miRNAs) and diseases is very important to understand the occurrence and development of human diseases. However, these existing methods suffer from the following limitation: first, some disease-related miRNAs are obtained from the miRNA functional similarity networks consisting of heterogeneous data sources, i.e., disease similarity, protein interaction network, gene expression. Second, little approaches infer disease-related miRNAs depending on the network topological features without the functional similarity of miRNAs. In this paper, we develop a novel model of Integrating Network Topology Similarity and MicroRNA Function Similarity (INTS-MFS). The integrated miRNA similarities are calculated based on miRNA functional similarity and network topological characteristics. INTS-MFS obtained AUC of 0.872 based on five-fold cross-validation and was applied to three common human diseases in case studies. As a results, 30 out of top 30 predicted Prostatic Neoplasm-related miRNAs were included in the two databases of dbDEMC and PhenomiR2.0. 29 out of top 30 predicted Lung Neoplasm-related miRNAs and Breast Neoplasm-related miRNAs were included in dbDEMC, PhenomiR2.0 and experimental reports. Moreover, INTS-MFS found unknown association with hsa-mir-371a in breast cancer and lung cancer, which have not been reported. It provides biologists new clues for diagnosing breast and lung cancer.

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APA Style
CAO, B., LUO, J., XIAO, S., ZHAO, K., YANG, S. (2022). INTS-MFS: A novel method to predict microrna-disease associations by integrating network topology similarity and microrna function similarity. BIOCELL, 46(3), 837-845. https://doi.org/10.32604/biocell.2022.017538
Vancouver Style
CAO B, LUO J, XIAO S, ZHAO K, YANG S. INTS-MFS: A novel method to predict microrna-disease associations by integrating network topology similarity and microrna function similarity. BIOCELL . 2022;46(3):837-845 https://doi.org/10.32604/biocell.2022.017538
IEEE Style
B. CAO, J. LUO, S. XIAO, K. ZHAO, and S. YANG "INTS-MFS: A novel method to predict microRNA-disease associations by integrating network topology similarity and microRNA function similarity," BIOCELL , vol. 46, no. 3, pp. 837-845. 2022. https://doi.org/10.32604/biocell.2022.017538



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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