The paper‘EDCNN: Identification of Genome-Wide RNA-binding Proteins Using Evolutionary Deep Convolutional Neural Network’ of the 2019 postgraduate student Yawei Wang under the guidance of Professor Xiangtao Li was accepted for publication in Bioinformatics. Bioinformatics is a top journal in bioinformatics with SCI IF (2020) = 6.937.
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.
