Prioritizing CircRNA-Disease Associations With Convolutional Neural Network Based on Multiple Similarity Feature Fusion

Frontiers in Genetics
Chunyan FanYi Pan

Abstract

Accumulating evidence shows that circular RNAs (circRNAs) have significant roles in human health and in the occurrence and development of diseases. Biological researchers have identified disease-related circRNAs that could be considered as potential biomarkers for clinical diagnosis, prognosis, and treatment. However, identification of circRNA-disease associations using traditional biological experiments is still expensive and time-consuming. In this study, we propose a novel method named MSFCNN for the task of circRNA-disease association prediction, involving two-layer convolutional neural networks on a feature matrix that fuses multiple similarity kernels and interaction features among circRNAs, miRNAs, and diseases. First, four circRNA similarity kernels and seven disease similarity kernels are constructed based on the biological or topological properties of circRNAs and diseases. Subsequently, the similarity kernel fusion method is used to integrate the similarity kernels into one circRNA similarity kernel and one disease similarity kernel, respectively. Then, a feature matrix for each circRNA-disease pair is constructed by integrating the fused circRNA similarity kernel and fused disease similarity kernel with interactions...Continue Reading

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Citations

Feb 9, 2021·Briefings in Functional Genomics·Yulian DingFang-Xiang Wu
Jun 19, 2021·The European Respiratory Journal·Jayanth Kumar NarayanaSanjay H Chotirmall

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Software Mentioned

DWNN
MF
Resnik
iCircDA
circAtlas
Python
SemFunSim
KATZHCDA
iCDA
Circ2Traits

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