DeepciRGO: functional prediction of circular RNAs through hierarchical deep neural networks using heterogeneous network features
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.
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 (PPIs) 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 (BP) function in the Gene Ontology (GO) 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 0.4, and an accuracy of 0.4, 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.
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Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF.
Abune ol
Lilek
Posted 27 Aug, 2020
On 25 Aug, 2020
On 24 Aug, 2020
On 23 Aug, 2020
On 23 Aug, 2020
Posted 10 Jan, 2020
On 12 Nov, 2020
On 12 Nov, 2020
On 25 Apr, 2020
Received 20 Apr, 2020
Received 17 Apr, 2020
Received 17 Apr, 2020
On 28 Mar, 2020
On 25 Mar, 2020
Received 24 Feb, 2020
On 12 Feb, 2020
On 12 Feb, 2020
On 17 Jan, 2020
Invitations sent on 17 Jan, 2020
On 07 Jan, 2020
On 06 Jan, 2020
On 23 Dec, 2019
Posted 10 Jan, 2020
On 14 Aug, 2020
Received 11 Aug, 2020
On 23 Jun, 2020
Invitations sent on 23 Jun, 2020
On 23 Jun, 2020
On 23 Jun, 2020
Received 23 Jun, 2020
On 22 Jun, 2020
On 22 Jun, 2020
DeepciRGO: functional prediction of circular RNAs through hierarchical deep neural networks using heterogeneous network features
Posted 27 Aug, 2020
On 25 Aug, 2020
On 24 Aug, 2020
On 23 Aug, 2020
On 23 Aug, 2020
Posted 10 Jan, 2020
On 12 Nov, 2020
On 12 Nov, 2020
On 25 Apr, 2020
Received 20 Apr, 2020
Received 17 Apr, 2020
Received 17 Apr, 2020
On 28 Mar, 2020
On 25 Mar, 2020
Received 24 Feb, 2020
On 12 Feb, 2020
On 12 Feb, 2020
On 17 Jan, 2020
Invitations sent on 17 Jan, 2020
On 07 Jan, 2020
On 06 Jan, 2020
On 23 Dec, 2019
Posted 10 Jan, 2020
On 14 Aug, 2020
Received 11 Aug, 2020
On 23 Jun, 2020
Invitations sent on 23 Jun, 2020
On 23 Jun, 2020
On 23 Jun, 2020
Received 23 Jun, 2020
On 22 Jun, 2020
On 22 Jun, 2020
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.
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 (PPIs) 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 (BP) function in the Gene Ontology (GO) 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 0.4, and an accuracy of 0.4, 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.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF.
Abune ol
Lilek