期刊
BIOINFORMATICS
卷 38, 期 3, 页码 678-686出版社
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btab739
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资金
- National Natural Science Foundation of China [62076109]
- Natural Science Foundation of Jilin Province [20190103006JH]
- Research Grants Council of the Hong Kong Special Administrative Region [CityU 11200218]
- Health and Medical Research Fund, the Food and Health Bureau, The Government of the Hong Kong Special Administrative Region [07181426]
In this study, a new algorithm, Evolutionary Deep Convolutional Neural Network (EDCNN), was proposed for identifying protein-RNA interactions. Experimental results demonstrated the superior performance of EDCNN on large-scale CLIP-seq datasets. Furthermore, analyses of time complexity, parameters, and motifs confirmed the effectiveness of the proposed algorithm.
Motivation: RNA-binding proteins (RBPs) are a group of proteins associated with RNA regulation and metabolism, and play an essential role in mediating the maturation, transport, localization and translation of RNA. Recently, Genome-wide RNA-binding event detection methods have been developed to predict RBPs. Unfortunately, the existing computational methods usually suffer some limitations, such as high-dimensionality, data sparsity and low model performance. Results: Deep convolution neural network has a useful advantage for solving high-dimensional and sparse data. To improve further the performance of deep convolution neural network, we propose evolutionary deep convolutional neural network (EDCNN) to identify protein-RNA interactions by synergizing evolutionary optimization with gradient descent to enhance deep conventional neural network. In particular, EDCNN combines evolutionary algorithms and different gradient descent models in a complementary algorithm, where the gradient descent and evolution steps can alternately optimize the RNA-binding event search. To validate the performance of EDCNN, an experiment is conducted on two large-scale CLIP-seq datasets, and results reveal that EDCNN provides superior performance to other state-of-the-art methods. Furthermore, time complexity analysis, parameter analysis and motif analysis are conducted to demonstrate the effectiveness of our proposed algorithm from several perspectives.
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