Combining High Speed ELM Learning with a Deep Convolutional Neural Network Feature Encoding for Predicting Protein-RNA Interactions
文献类型:期刊论文
作者 | Wang, L (Wang, Lei)[ 1 ]; You, ZH (You, Zhu-Hong)[ 2 ]; Huang, DS (Huang, De-Shuang)[ 3 ]; Zhou, FF (Zhou, Fengfeng)[ 4,5 ] |
刊名 | IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS |
出版日期 | 2020 |
卷号 | 17期号:3页码:972-980 |
ISSN号 | 1545-5963 |
关键词 | Proteins RNA Convolution Neural networks Benchmark testing Feature extraction Sparse matrices Convolution neural network extreme learning machine RNA-protein interactions sequence |
DOI | 10.1109/TCBB.2018.2874267 |
英文摘要 | 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. |
WOS记录号 | WOS:000542948000025 |
源URL | [http://ir.xjipc.cas.cn/handle/365002/7673] |
专题 | 新疆理化技术研究所_多语种信息技术研究室 |
通讯作者 | You, ZH (You, Zhu-Hong)[ 2 ] |
作者单位 | 1.Jilin Univ, Minist Educ, Key Lab Symbol Computat & Knowledge Engn, Changchun 130012, Jilin, Peoples R China 2.Jilin Univ, Minist Educ, Coll Comp Sci & Technol, Changchun 130012, Jilin, Peoples R China 3.Tongji Univ, Inst Machine Learning & Syst Biol, Sch Elect & Informat Engn, Caoan Rd 4800, Shanghai 201804, Peoples R China 4.Chinese Acad Sci, Xinjiang Tech Inst Phys & Chem, Urumqi 830011, Peoples R China 5.Zaozhuang Univ, Coll Informat Sci & Engn, Zaozhuang 277100, Peoples R China |
推荐引用方式 GB/T 7714 | Wang, L ,You, ZH ,Huang, DS ,et al. Combining High Speed ELM Learning with a Deep Convolutional Neural Network Feature Encoding for Predicting Protein-RNA Interactions[J]. IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS,2020,17(3):972-980. |
APA | Wang, L ,You, ZH ,Huang, DS ,&Zhou, FF .(2020).Combining High Speed ELM Learning with a Deep Convolutional Neural Network Feature Encoding for Predicting Protein-RNA Interactions.IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS,17(3),972-980. |
MLA | Wang, L ,et al."Combining High Speed ELM Learning with a Deep Convolutional Neural Network Feature Encoding for Predicting Protein-RNA Interactions".IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 17.3(2020):972-980. |
入库方式: OAI收割
来源:新疆理化技术研究所
其他版本
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。