BiRWLGO: A global network-based strategy for lncRNA function annotation using bi-random walk

Zhang, Jingpu; Zou, Shuai; Deng, Lei

doi:10.1109/bibm.2017.8217623

Cited by 3 publications

(1 citation statement)

References 38 publications

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“…To better assess the performance of DeepciRGO, we compare it with three existing models: KATZLGO [30], PmiRGO [31] and BiRWLGO [32]. KATZLGO and BiRWLGO are two link-based prediction methods that infer the functions of RNAs by calculating the correlation scores between lncRNAs and proteins in a global network.…”

Section: Performancesmentioning

confidence: 99%

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

et al. 2020

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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.

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Section: Performancesmentioning

confidence: 99%

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

et al. 2020

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DKCirc2GO: Predicting Gene Ontology of circRNAs Using Dual KATZ Approach

Deng

Sun

Lin

et al. 2019

2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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Prediction of lncRNA functions using deep neural networks based on multiple networks

Deng,

Ren,

Zhang

2023

BMC Genomics

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Background More and more studies show that lncRNA is widely involved in various physiological processes of the organism. However, the functions of the vast majority of them continue to be unknown. In addition, data related to lncRNAs in biological databases are constantly increasing. Therefore, it is quite urgent to develop a computing method to make the utmost of these data. Results In this paper, we propose a new computational method based on global heterogeneous networks to predict the functions of lncRNAs, called DNGRGO. DNGRGO first calculates the similarities among proteins, miRNAs, and lncRNAs, and annotates the functions of lncRNAs according to its similar protein-coding genes, which have been labeled with gene ontology (GO). To evaluate the performance of DNGRGO, we manually annotated GO terms to lncRNAs and implemented our method on these data. Compared with the existing methods, the results of DNGRGO show superior predictive performance of maximum F-measure and coverage. Conclusions DNGRGO is able to annotate lncRNAs through capturing the low-dimensional features of the heterogeneous network. Moreover, the experimental results show that integrating miRNA data can help to improve the predictive performance of DNGRGO.

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BiRWLGO: A global network-based strategy for lncRNA function annotation using bi-random walk

Cited by 3 publications

References 38 publications

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

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

DKCirc2GO: Predicting Gene Ontology of circRNAs Using Dual KATZ Approach

Prediction of lncRNA functions using deep neural networks based on multiple networks

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