Journal
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
Volume 17, Issue 3, Pages 972-980Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TCBB.2018.2874267
Keywords
Proteins; RNA; Convolution; Neural networks; Benchmark testing; Feature extraction; Sparse matrices; Convolution neural network; extreme learning machine; RNA-protein interactions; sequence
Categories
Funding
- NSFC Excellent Young Scholars Program [61722212]
- Pioneer Hundred Talents Program of Chinese Academy of Sciences
- Natural Science Foundation of China [61572506, 61702444, 61732012]
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Emerging evidence has shown that RNA plays a crucial role in many cellular processes, and their biological functions are primarily achieved by binding with a variety of proteins. High-throughput biological experiments provide a lot of valuable information for the initial identification of RNA-protein interactions (RPIs), but with the increasing complexity of RPIs networks, this method gradually falls into expensive and time-consuming situations. Therefore, there is an urgent need for high speed and reliable methods to predict RNA-protein interactions. In this study, we propose a computational method for predicting the RNA-protein interactions using sequence information. The deep learning convolution neural network (CNN) algorithm is utilized to mine the hidden high-level discriminative features from the RNA and protein sequences and feed it into the extreme learning machine (ELM) classifier. The experimental results with 5-fold cross-validation indicate that the proposed method achieves superior performance on benchmark datasets (RPI1807, RPI2241, and RPI369) with the accuracy of 98.83, 90.83, and 85.63 percent, respectively. We further evaluate the performance of the proposed model by comparing it with the state-of-the-art SVM classifier and other existing methods on the same benchmark data set. In addition, we predicted the independent NPInter v2.0 data set using the model trained on RPI369. The experimental results show that our model can serve as a useful tool for predicting RNA-protein interactions.
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