Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/143993
Title: Classifying promoters by interpreting the hidden information of DNA sequences via deep learning and combination of continuous FastText N-grams
Authors: Le, Nguyen Quoc Khanh
Yapp, Edward Kien Yee
Nagasundaram, Nagarajan
Yeh, Hui-Yuan
Keywords: Humanities::General
Issue Date: 2019
Source: Le, N. Q. K., Yapp, E. K. Y., Nagasundaram, N., & Yeh, H.-Y. (2019). Classifying promoters by interpreting the hidden information of DNA sequences via deep learning and combination of continuous FastText N-grams. Frontiers in Bioengineering and Biotechnology, 7, 305-. doi:10.3389/fbioe.2019.00305
Journal: Frontiers in Bioengineering and Biotechnology
Abstract: A promoter is a short region of DNA (100-1,000 bp) where transcription of a gene by RNA polymerase begins. It is typically located directly upstream or at the 5' end of the transcription initiation site. DNA promoter has been proven to be the primary cause of many human diseases, especially diabetes, cancer, or Huntington's disease. Therefore, classifying promoters has become an interesting problem and it has attracted the attention of a lot of researchers in the bioinformatics field. There were a variety of studies conducted to resolve this problem, however, their performance results still require further improvement. In this study, we will present an innovative approach by interpreting DNA sequences as a combination of continuous FastText N-grams, which are then fed into a deep neural network in order to classify them. Our approach is able to attain a cross-validation accuracy of 85.41 and 73.1% in the two layers, respectively. Our results outperformed the state-of-the-art methods on the same dataset, especially in the second layer (strength classification). Throughout this study, promoter regions could be identified with high accuracy and it provides analysis for further biological research as well as precision medicine. In addition, this study opens new paths for the natural language processing application in omics data in general and DNA sequences in particular.
URI: https://hdl.handle.net/10356/143993
ISSN: 2296-4185
DOI: 10.3389/fbioe.2019.00305
Rights: © 2019 Le, Yapp, Nagasundaram and Yeh. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SoH Journal Articles

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