DeepciRGO: functional prediction of circular RNAs through hierarchical deep neural networks using heterogeneous network features

Abstract

Background Circular RNAs (circRNAs) are special noncoding RNA molecules with closed loop structures. Compared with the traditional linear RNA, circRNA is more stable and not easily degraded. Many studies have shown that circRNAs are involved in the regulation of various diseases and cancers. Determining the functions of circRNAs in mammalian cells is of great significance for revealing their mechanism of action in physiological and pathological processes, diagnosis and treatment of diseases. However, determining the functions of circRNAs on a large scale is a challenging task because of the high experimental costs. Results In this paper, we present a hierarchical deep learning model, DeepciRGO, which can effectively predict gene ontology functions of circRNAs. We build a heterogeneous network containing circRNA co-expressions, protein–protein interactions and protein–circRNA interactions. The topology features of proteins and circRNAs are calculated using a novel representation learning approach HIN2Vec across the heterogeneous network. Then, a deep multi-label hierarchical classification model is trained with the topology features to predict the biological process function in the gene ontology for each circRNA. In particular, we manually curated a benchmark dataset containing 185 GO annotations for 62 circRNAs, namely, circRNA2GO-62. The DeepciRGO achieves promising performance on the circRNA2GO-62 dataset with a maximum F-measure of 0.412, a recall score of 0.400, and an accuracy of 0.425, which are significantly better than other state-of-the-art RNA function prediction methods. In addition, we demonstrate the considerable potential of integrating multiple interactions and association networks. Conclusions DeepciRGO will be a useful tool for accurately annotating circRNAs. The experimental results show that integrating multi-source data can help to improve the predictive performance of DeepciRGO. Moreover, The model also can combine RNA structure and sequence information to further optimize predictive performance.

COL: a pipeline for identifying putatively functional back-splicing

“…Thus, if the miRNA-or protein-binding motif in a circRNA is evolutionarily conserved, the circRNA and the corresponding back-splicing are likely functional. Indeed, some programs and webservers use binding information to predict circRNA functionality [37][38][39]. However, due to frequent alternative splicing inside circRNAs [40], the sequence and length of the circRNAs resulting from the same back-splicing are variable.…”

Epigenetic Regulations in Autoimmunity and Cancer: from Basic Science to Translational Medicine

“…CircRNAs are special noncoding RNAs with stable loop structures. It is challenging to study the structure and function of circRNAs, but many learning models, databases and other tools have been proposed [52,53]. CircRNAs have shown their potential as effective and appropriate targets, paving the way for the clinical treatment of diverse diseases.…”

A Brief Review of circRNA Biogenesis, Detection, and Function

“…develop a new classifier, CirRNAPL, which extracts the features of nucleic acid composition and structure of the circRNA sequence to optimize the extreme learning machine (ELM), based on the particle swarm optimization algorithm; ELM algorithm randomly assigns input weights and hidden layer thresholds and directly calculates output layer weights by least squares, PSO is used to optimize the input weight and hidden layer deviation of ELM, which can improve the generalizability of the methods [ 59 ]. DeepciRGO is constructed using the dependencies between GO classes as background information to predict circRNA functions by integrating multiple interactions and associations ; the first step is to extract the topological information of each node in the global network as its feature; and then build a neural network for each GO, consider the functional dependencies between the classes in GO [ 60 ].…”