A Novel Prediction Method for ATP-Binding Sites From Protein Primary Sequences Based on Fusion of Deep Convolutional Neural Network and Ensemble Learning
| Autor: | Yanchun Liang, Ping Zhang, Rongquan Wang, Guixia Liu, Liyan Sun, Jiazhi Song |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Source code General Computer Science Computer science media_common.quotation_subject 0206 medical engineering 02 engineering and technology Convolutional neural network 03 medical and health sciences deep convolutional neural network inception neural network General Materials Science media_common Protein-ATP binding sites prediction Sequence Network architecture business.industry Deep learning General Engineering Pattern recognition Ensemble learning Statistical classification 030104 developmental biology Benchmark (computing) ensemble learning Artificial intelligence lcsh:Electrical engineering. Electronics. Nuclear engineering protein primary sequence business lcsh:TK1-9971 020602 bioinformatics |
| Zdroj: | IEEE Access, Vol 8, Pp 21485-21495 (2020) |
| ISSN: | 2169-3536 |
| Popis: | Accurately identifying protein-ATP (Adenosine-5’-triphosphate) binding sites is significant for protein function annotation and new drug invention. Previous studies often utilize classical machine learning classification algorithms to predict protein-ATP binding sites based on protein primary sequence. However, deep learning as a newly developed technique has shown outstanding performance in various fields. In this work, we introduce the deep convolutional neural network for protein-ATP binding sites prediction based on sequence information. Two classification networks are developed including a residual-inception-based predictor and a multi-inception-based predictor, then the ensemble learning is applied by giving optimized weights to each network architecture to combine them for more superior performance. We examine the performance of our proposed method on two groups of independent testing sets including a classic ATP-binding benchmark dataset and a newly proposed ATP-binding dataset. As a result, our proposed method outperforms other state-of-art sequence-based predictors with the AUC of 0.922 and 0.896 respectively which illustrates the efficacy of deep learning technique in protein-ATP binding sites prediction. The source code and benchmark datasets can be downloaded at https://github.com/tlsjz/ATPbinding . |
| Databáze: | OpenAIRE |
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