Prediction of MiRNA-disease Associations with a Vector Space Model

Overview

Journal Sci Rep

Specialty Science

Date 2016 Jun 2

PMID 27246786

Citations 55

Authors

Claude Pasquier

Julien Gardes

Affiliations

Soon will be listed here.

Abstract

MicroRNAs play critical roles in many physiological processes. Their dysregulations are also closely related to the development and progression of various human diseases, including cancer. Therefore, identifying new microRNAs that are associated with diseases contributes to a better understanding of pathogenicity mechanisms. MicroRNAs also represent a tremendous opportunity in biotechnology for early diagnosis. To date, several in silico methods have been developed to address the issue of microRNA-disease association prediction. However, these methods have various limitations. In this study, we investigate the hypothesis that information attached to miRNAs and diseases can be revealed by distributional semantics. Our basic approach is to represent distributional information on miRNAs and diseases in a high-dimensional vector space and to define associations between miRNAs and diseases in terms of their vector similarity. Cross validations performed on a dataset of known miRNA-disease associations demonstrate the excellent performance of our method. Moreover, the case study focused on breast cancer confirms the ability of our method to discover new disease-miRNA associations and to identify putative false associations reported in databases.

Citing Articles

DAEMDA: A Method with Dual-Channel Attention Encoding for miRNA-Disease Association Prediction.

Dong B, Sun W, Xu D, Wang G, Zhang T Biomolecules. 2023; 13(10).

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GDCL-NcDA: identifying non-coding RNA-disease associations via contrastive learning between deep graph learning and deep matrix factorization.

Ai N, Liang Y, Yuan H, Ouyang D, Xie S, Liu X BMC Genomics. 2023; 24(1):424.

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KATZNCP: a miRNA-disease association prediction model integrating KATZ algorithm and network consistency projection.

Chen M, Deng Y, Li Z, Ye Y, He Z BMC Bioinformatics. 2023; 24(1):229.

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MiRNA-Seq reveals key MicroRNAs involved in fat metabolism of sheep liver.

Fei X, Jin M, Yuan Z, Li T, Lu Z, Wang H Front Genet. 2023; 14:985764.

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MEAHNE: miRNA-Disease Association Prediction Based on Semantic Information in a Heterogeneous Network.

Huang C, Cen K, Zhang Y, Liu B, Wang Y, Li J Life (Basel). 2022; 12(10).

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