Potential circRNA-Disease Association Prediction Using DeepWalk and Nonnegative Matrix Factorization | IEEE Journals & Magazine | IEEE Xplore

Potential circRNA-Disease Association Prediction Using DeepWalk and Nonnegative Matrix Factorization

Publisher: IEEE

Abstract:

Circular RNAs (circRNAs) are a category of noncoding RNAs that exist in great numbers in eukaryotes. They have recently been discovered to be crucial in the growth of tum...View more

Abstract:

Circular RNAs (circRNAs) are a category of noncoding RNAs that exist in great numbers in eukaryotes. They have recently been discovered to be crucial in the growth of tumors. Therefore, it is important to explore the association of circRNAs with disease. This paper proposes a new method based on DeepWalk and nonnegative matrix factorization (DWNMF) to predict circRNA-disease association. Based on the known circRNA-disease association, we calculate the topological similarity of circRNA and disease via the DeepWalk-based method to learn the node features on the association network. Next, the functional similarity of the circRNAs and the semantic similarity of the diseases are fused with their respective topological similarities at different scales. Then, we use the improved weighted K -nearest neighbor (IWKNN) method to preprocess the circRNA-disease association network and correct nonnegative associations by setting different parameters K1 and K2 in the circRNA and disease matrices. Finally, the L2,1 -norm, dual-graph regularization term and Frobenius norm regularization term are introduced into the nonnegative matrix factorization model to predict the circRNA-disease correlation. We perform cross-validation on circR2Disease, circRNADisease, and MNDR. The numerical results show that DWNMF is an efficient tool for forecasting potential circRNA-disease relationships, outperforming other state-of-the-art approaches in terms of predictive performance.
Published in: IEEE/ACM Transactions on Computational Biology and Bioinformatics ( Volume: 20, Issue: 5, 01 Sept.-Oct. 2023)
Page(s): 3154 - 3162
Date of Publication: 05 April 2023

ISSN Information:

PubMed ID: 37018084
Publisher: IEEE

Funding Agency:


I Introduction

Circular RNA (circRNA) is a type of noncoding RNA that differs from linear RNA. It was initially found in 1976 in the Sendai virus [1]. CircRNAs have a covalent closed loop structure, without 5’ cap and 3’ polyadenylated tails [2]. Subsequent studies have identified the presence of circular RNA in humans [3], mice [4], fungi, and a variety of other tissues and cells. Due to the limitations of earlier techniques, researchers initially believed that circRNA had no biological importance and was simply a transcriptional consequence of aberrant splicing [4], [5]. CircRNAs have been revealed to play a crucial role in the regulation of gene expression through developments in high-throughput sequencing methods and associated studies [6], [7].

References

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