Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/150965
Title: iEnhancer-5step : identifying enhancers using hidden information of DNA sequences via Chou's 5-step rule and word embedding
Authors: Le, Nguyen Quoc Khanh
Yapp, Edward Kien Yee
Ho, Quang-Thai
Nagasundaram, Nagarajan
Ou, Yu-Yen
Yeh, Hui-Yuan
Keywords: Science::Biological sciences
Issue Date: 2019
Source: Le, N. Q. K., Yapp, E. K. Y., Ho, Q., Nagasundaram, N., Ou, Y. & Yeh, H. (2019). iEnhancer-5step : identifying enhancers using hidden information of DNA sequences via Chou's 5-step rule and word embedding. Analytical Biochemistry, 571, 53-61. https://dx.doi.org/10.1016/j.ab.2019.02.017
Journal: Analytical Biochemistry
Abstract: An enhancer is a short (50–1500bp) region of DNA that plays an important role in gene expression and the production of RNA and proteins. Genetic variation in enhancers has been linked to many human diseases, such as cancer, disorder or inflammatory bowel disease. Due to the importance of enhancers in genomics, the classification of enhancers has become a popular area of research in computational biology. Despite the few computational tools employed to address this problem, their resulting performance still requires improvements. In this study, we treat enhancers by the word embeddings, including sub-word information of its biological words, which then serve as features to be fed into a support vector machine algorithm to classify them. We present iEnhancer-5Step, a web server containing two-layer classifiers to identify enhancers and their strength. We are able to attain an independent test accuracy of 79% and 63.5% in the two layers, respectively. Compared to current predictors on the same dataset, our proposed method is able to yield superior performance as compared to the other methods. Moreover, this study provides a basis for further research that can enrich the field of applying natural language processing techniques in biological sequences. iEnhancer-5Step is freely accessible via http://biologydeep.com/fastenc/.
URI: https://hdl.handle.net/10356/150965
ISSN: 0003-2697
DOI: 10.1016/j.ab.2019.02.017
Rights: © 2019 Elsevier Inc. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SoH Journal Articles

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