MSFSP: A Novel miRNA-Disease Association Prediction Model by Federating Multiple-Similarities Fusion and Space Projection.

Yi Zhang,Hanyan Wei,Min Chen,Xiaohui Cheng

doi:10.3389/fgene.2020.00389

Abstract

Growing evidences have indicated that microRNAs (miRNAs) play a significant role relating to many important bioprocesses; their mutations and disorders will cause the occurrence of various complex diseases. The prediction of miRNAs associated with underlying diseases via computational approaches is beneficial to identify biomarkers and discover specific medicine, which can greatly reduce the cost of diagnosis, cure, prognosis, and prevention of human diseases. However, how to further achieve a more reliable prediction of potential miRNA–disease associations with effective integration of different biological data is a challenge for researchers. In this study, we proposed a computational model by using a federated method of combined multiple-similarities fusion and space projection (MSFSP). MSFSP firstly fused the integrated disease similarity (composed of disease semantic similarity, disease functional similarity, and disease Hamming similarity) with the integrated miRNA similarity (composed of miRNA functional similarity, miRNA sequence similarity, and miRNA Hamming similarity). Secondly, it constructed the weighted network of miRNA–disease associations from the experimentally verified Boolean network of miRNA–disease associations by using similarity networks. Finally, it calculated the prediction results by weighting miRNA space projection scores and the disease space projection scores. Leave-one-out cross-validation demonstrated that MSFSP has the distinguished predictive accuracy with area under the receiver operating characteristics curve (AUC) of 0.9613 better than that of five other existing models. In case studies, the predictive ability of MSFSP was further confirmed as 96 and 98% of the top 50 predictions for prostatic neoplasms and lung neoplasms were successfully validated by experimental evidences and supporting experimental evidences were also found for 100% of the top 50 predictions for isolated diseases.

Highlights

The microRNAs widely found in eukaryotes are those non-coding RNAs of about 20–25 nucleotides (Iorio et al, 2005)
We found the association between hsa-mir-384 and lung neoplasms by searching the latest literature (Guo et al, 2019) whose publication date was after the last update of HMDD v3.2, which further confirmed the effectiveness of multiple-similarities fusion and space projection (MSFSP) for inferring diseases potentially related to miRNAs
The reliable performance of MSFSP achieved can be attributed to the following factors: (1) Different biological information data were fused in MSFSP to construct the integrated miRNA similarity network and the integrated disease similarity network; (2) More accurate miRNA–disease correlations were described by weighted networks that were integrated with the disease similarity network, the miRNA similarity network, and the experimentally verified Boolean network of miRNA–disease associations; (3) MiRNA space projection scores and disease space projection scores were combined to obtain the final prediction scores, which avoided the invalid inference for new miRNAs only with disease space projection scores and the invalid inference for isolated diseases only with miRNA space projection scores

Summary

Introduction

The microRNAs (miRNAs) widely found in eukaryotes are those non-coding RNAs of about 20–25 nucleotides (Iorio et al, 2005) Life processes such as cell growth (Fernando et al, 2012; Zhu et al, 2016), differentiation (Miska, 2005), proliferation (Cheng et al, 2005), aging (Xu et al, 2004), signal transduction (Carthew and Sontheimer, 2009), etc. Predicting and ranking potential miRNA–disease associations effectively and rapidly via computational identification methods are extremely vital to speed up the bio-experimental validation processes as well as reduce the blindness and time consumption of bio-experiments (Chen et al, 2015c, 2019c; Zeng et al, 2016b; Peng et al, 2017a, 2020)

Methods

Results

Conclusion