Identifying Enhancers and Their Strength by the Integration of Word Embedding and Convolution Neural Network
The enhancer is a short regulatory element that plays a major role in up-regulating eukaryotic gene expression. To identify enhancers, an experimental process takes a long time and high cost; therefore, an accurate computational tool is a much-needed work in this area. Existing techniques were devel...
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doaj-955fad8826b64cfbbb444b9f893729022021-03-30T02:56:07ZengIEEEIEEE Access2169-35362020-01-018583695837610.1109/ACCESS.2020.29826669044822Identifying Enhancers and Their Strength by the Integration of Word Embedding and Convolution Neural NetworkJhabindra Khanal0https://orcid.org/0000-0001-6470-1365Hilal Tayara1https://orcid.org/0000-0001-5678-3479Kil To Chong2https://orcid.org/0000-0002-1952-0001Department of Electronics and Information Engineering, Chonbuk National University, Jeonju, South KoreaDepartment of Electronics and Information Engineering, Chonbuk National University, Jeonju, South KoreaAdvanced Electronics and Information Research Center, Chonbuk National University, Jeonju, South KoreaThe enhancer is a short regulatory element that plays a major role in up-regulating eukaryotic gene expression. To identify enhancers, an experimental process takes a long time and high cost; therefore, an accurate computational tool is a much-needed work in this area. Existing techniques were developed by the use of handcrafted features followed by machine learning techniques, while the proposed model extracts the features of enhancers from raw DNA sequences by the integration of natural language processing (NLP) technique using word2vec and convolutional neural network (CNN). Therefore, an accurate computational tool, iEnhancer-CNN, is developed. The developed tool can predict enhancers and their strength. The evaluation results show that iEnhancer-CNN is remarkably superior to the existing state-of-the-art models. In more detail, iEnhancer-CNN improved the accuracy of enhancer and enhancer strength identification by 2.6% and 11.4%, respectively. A web server for the iEnhancer-CNN is freely available at https://home.jbnu.ac.kr/NSCL/iEnhancer-CNN.htm.https://ieeexplore.ieee.org/document/9044822/Convolutional neural networkDNA sequencedeep learningenhancersK-mersword2vec |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jhabindra Khanal Hilal Tayara Kil To Chong |
spellingShingle |
Jhabindra Khanal Hilal Tayara Kil To Chong Identifying Enhancers and Their Strength by the Integration of Word Embedding and Convolution Neural Network IEEE Access Convolutional neural network DNA sequence deep learning enhancers K-mers word2vec |
author_facet |
Jhabindra Khanal Hilal Tayara Kil To Chong |
author_sort |
Jhabindra Khanal |
title |
Identifying Enhancers and Their Strength by the Integration of Word Embedding and Convolution Neural Network |
title_short |
Identifying Enhancers and Their Strength by the Integration of Word Embedding and Convolution Neural Network |
title_full |
Identifying Enhancers and Their Strength by the Integration of Word Embedding and Convolution Neural Network |
title_fullStr |
Identifying Enhancers and Their Strength by the Integration of Word Embedding and Convolution Neural Network |
title_full_unstemmed |
Identifying Enhancers and Their Strength by the Integration of Word Embedding and Convolution Neural Network |
title_sort |
identifying enhancers and their strength by the integration of word embedding and convolution neural network |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
The enhancer is a short regulatory element that plays a major role in up-regulating eukaryotic gene expression. To identify enhancers, an experimental process takes a long time and high cost; therefore, an accurate computational tool is a much-needed work in this area. Existing techniques were developed by the use of handcrafted features followed by machine learning techniques, while the proposed model extracts the features of enhancers from raw DNA sequences by the integration of natural language processing (NLP) technique using word2vec and convolutional neural network (CNN). Therefore, an accurate computational tool, iEnhancer-CNN, is developed. The developed tool can predict enhancers and their strength. The evaluation results show that iEnhancer-CNN is remarkably superior to the existing state-of-the-art models. In more detail, iEnhancer-CNN improved the accuracy of enhancer and enhancer strength identification by 2.6% and 11.4%, respectively. A web server for the iEnhancer-CNN is freely available at https://home.jbnu.ac.kr/NSCL/iEnhancer-CNN.htm. |
topic |
Convolutional neural network DNA sequence deep learning enhancers K-mers word2vec |
url |
https://ieeexplore.ieee.org/document/9044822/ |
work_keys_str_mv |
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