Prediction of circRNA-disease associations based on inductive matrix completion

Menglu Li,Mengya Liu,Yannan Bin,Junfeng Xia

doi:10.1186/s12920-020-0679-0

Abstract

BackgroundCurrently, numerous studies indicate that circular RNA (circRNA) is associated with various human complex diseases. While identifying disease-related circRNAs in vivo is time- and labor-consuming, a feasible and effective computational method to predict circRNA-disease associations is worthy of more studies.ResultsHere, we present a new method called SIMCCDA (Speedup Inductive Matrix Completion for CircRNA-Disease Associations prediction) to predict circRNA-disease associations. Based on known circRNA-disease associations, circRNA sequence similarity, disease semantic similarity, and the computed Gaussian interaction profile kernel similarity, we used speedup inductive matrix completion to construct the model. The proposed SIMCCDA method obtains an area under ROC curve (AUC) of 0.8465 with leave-one-out cross validation in the dataset, which is obtained by the combination of the three databases (circRNA disease, circ2Disease and circR2Disease). Our method surpasses other state-of-art models in predicting circRNA-disease associations. Furthermore, we conducted case studies in breast cancer, stomach cancer and colorectal cancer for further performance evaluation.ConclusionAll the results show reliable prediction ability of SIMCCDA. We anticipate that SIMCCDA could be utilized to facilitate further developments in the field and follow-up investigations by biomedical researchers.

Highlights

Numerous studies indicate that circular RNA is associated with various human complex diseases
Leave-one-out cross validation (LOOCV) To assess the predictive accuracy of SIMCCDA, we performed the following method using the leave-one-out cross validation (LOOCV) framework on the known circular RNA (circRNA)-disease associations
The reason why LOOCV is used in this study is that the current common practice in this field [30,31,32] is to use LOOCV to measure the performance of the model

Summary

Introduction

Numerous studies indicate that circular RNA (circRNA) is associated with various human complex diseases. The largest difference is that the circRNA does not possess a terminal structure (i.e., 5′ caps and 3′ polyA tails) and is covalently closed to form a loop structure [1] Such a loop structure facilitates the resistance of the circRNA to the degradation of RNA exonuclease and offers a stable biological effect compared with the corresponding linear structure [2, 3]. Developing computational methods to predict novel circRNA-disease associations has attracted considerable attention as they can effectively decrease the time and cost of biological experiments. Lei et al [11] developed the method of predicting circRNA-disease associations based on a path weighted model, and Fan et al [12] proposed the KATZHCDA method using the KATZ model on heterogeneous networks. It remains challenging to achieve significant performance for predicting circRNA-disease associations

Methods

Results

Conclusion