iEnhancer-ECNN: identifying enhancers and their strength using ensembles of convolutional neural networks
Identifieur interne : 000306 ( Pmc/Curation ); précédent : 000305; suivant : 000307iEnhancer-ECNN: identifying enhancers and their strength using ensembles of convolutional neural networks
Auteurs : Quang H. Nguyen [Viêt Nam] ; Thanh-Hoang Nguyen-Vo [Nouvelle-Zélande] ; Nguyen Quoc Khanh Le ; Trang T. T. Do [Viêt Nam] ; Susanto Rahardja [République populaire de Chine] ; Binh P. Nguyen [Nouvelle-Zélande]Source :
- BMC Genomics [ 1471-2164 ] ; 2019.
Abstract
Enhancers are non-coding DNA fragments which are crucial in gene regulation (e.g. transcription and translation). Having high locational variation and free scattering in 98% of non-encoding genomes, enhancer identification is, therefore, more complicated than other genetic factors. To address this biological issue, several in silico studies have been done to identify and classify enhancer sequences among a myriad of DNA sequences using computational advances. Although recent studies have come up with improved performance, shortfalls in these learning models still remain. To overcome limitations of existing learning models, we introduce iEnhancer-ECNN, an efficient prediction framework using one-hot encoding and
Our experimental results demonstrates that iEnhancer-ECNN has better performance compared to other state-of-the-art methods using the same dataset. The accuracy of the ensemble model for enhancer identification (layer 1) and enhancer classification (layer 2) are 0.769 and 0.678, respectively. Compared to other related studies, improvements in the Area Under the Receiver Operating Characteristic Curve (AUC), sensitivity, and Matthews’s correlation coefficient (MCC) of our models are remarkable, especially for the model of layer 2 with about 11.0%, 46.5%, and 65.0%, respectively.
iEnhancer-ECNN outperforms other previously proposed methods with significant improvement in most of the evaluation metrics. Strong growths in the MCC of both layers are highly meaningful in assuring the stability of our models.
Url:
DOI: 10.1186/s12864-019-6336-3
PubMed: 31874637
PubMed Central: 6929481
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<front><div type="abstract" xml:lang="en"><sec><title>Background</title>
<p id="Par1">Enhancers are non-coding DNA fragments which are crucial in gene regulation (e.g. transcription and translation). Having high locational variation and free scattering in 98% of non-encoding genomes, enhancer identification is, therefore, more complicated than other genetic factors. To address this biological issue, several in silico studies have been done to identify and classify enhancer sequences among a myriad of DNA sequences using computational advances. Although recent studies have come up with improved performance, shortfalls in these learning models still remain. To overcome limitations of existing learning models, we introduce iEnhancer-ECNN, an efficient prediction framework using one-hot encoding and <italic>k</italic>
-mers for data transformation and ensembles of convolutional neural networks for model construction, to identify enhancers and classify their strength. The benchmark dataset from Liu et al.’s study was used to develop and evaluate the ensemble models. A comparative analysis between iEnhancer-ECNN and existing state-of-the-art methods was done to fairly assess the model performance.</p>
</sec>
<sec><title>Results</title>
<p id="Par2">Our experimental results demonstrates that iEnhancer-ECNN has better performance compared to other state-of-the-art methods using the same dataset. The accuracy of the ensemble model for enhancer identification (layer 1) and enhancer classification (layer 2) are 0.769 and 0.678, respectively. Compared to other related studies, improvements in the Area Under the Receiver Operating Characteristic Curve (AUC), sensitivity, and Matthews’s correlation coefficient (MCC) of our models are remarkable, especially for the model of layer 2 with about 11.0%, 46.5%, and 65.0%, respectively.</p>
</sec>
<sec><title>Conclusions</title>
<p id="Par3">iEnhancer-ECNN outperforms other previously proposed methods with significant improvement in most of the evaluation metrics. Strong growths in the MCC of both layers are highly meaningful in assuring the stability of our models.</p>
</sec>
</div>
</front>
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<pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">BMC Genomics</journal-id>
<journal-id journal-id-type="iso-abbrev">BMC Genomics</journal-id>
<journal-title-group><journal-title>BMC Genomics</journal-title>
</journal-title-group>
<issn pub-type="epub">1471-2164</issn>
<publisher><publisher-name>BioMed Central</publisher-name>
<publisher-loc>London</publisher-loc>
</publisher>
</journal-meta>
<article-meta><article-id pub-id-type="pmid">31874637</article-id>
<article-id pub-id-type="pmc">6929481</article-id>
<article-id pub-id-type="publisher-id">6336</article-id>
<article-id pub-id-type="doi">10.1186/s12864-019-6336-3</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Research</subject>
</subj-group>
</article-categories>
<title-group><article-title>iEnhancer-ECNN: identifying enhancers and their strength using ensembles of convolutional neural networks</article-title>
</title-group>
<contrib-group><contrib contrib-type="author"><name><surname>Nguyen</surname>
<given-names>Quang H.</given-names>
</name>
<address><email>quangnh@soict.hust.edu.vn</email>
</address>
<xref ref-type="aff" rid="Aff1">1</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Nguyen-Vo</surname>
<given-names>Thanh-Hoang</given-names>
</name>
<address><email>thanhhoang.nguyenvo@vuw.ac.nz</email>
</address>
<xref ref-type="aff" rid="Aff2">2</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Le</surname>
<given-names>Nguyen Quoc Khanh</given-names>
</name>
<address><email>khanhlee@tmu.edu.tw</email>
</address>
<xref ref-type="aff" rid="Aff3">3</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Do</surname>
<given-names>Trang T.T.</given-names>
</name>
<address><email>dotrang@alumni.nus.edu.sg</email>
</address>
<xref ref-type="aff" rid="Aff4">4</xref>
</contrib>
<contrib contrib-type="author" corresp="yes"><name><surname>Rahardja</surname>
<given-names>Susanto</given-names>
</name>
<address><email>susantorahardja@ieee.org</email>
</address>
<xref ref-type="aff" rid="Aff5">5</xref>
</contrib>
<contrib contrib-type="author" corresp="yes"><name><surname>Nguyen</surname>
<given-names>Binh P.</given-names>
</name>
<address><email>binh.p.nguyen@vuw.ac.nz</email>
</address>
<xref ref-type="aff" rid="Aff2">2</xref>
</contrib>
<aff id="Aff1"><label>1</label>
<institution-wrap><institution-id institution-id-type="GRID">grid.440792.c</institution-id>
<institution>School of Information and Communication Technology, Hanoi University of Science and Technology,</institution>
</institution-wrap>
1 Dai Co Viet, Hanoi 100000, Vietnam</aff>
<aff id="Aff2"><label>2</label>
<institution-wrap><institution-id institution-id-type="ISNI">0000 0001 2292 3111</institution-id>
<institution-id institution-id-type="GRID">grid.267827.e</institution-id>
<institution>School of Mathematics and Statistics, Victoria University of Wellington,</institution>
</institution-wrap>
Gate 7, Kelburn Parade, Wellington, 6142 New Zealand</aff>
<aff id="Aff3"><label>3</label>
<institution-wrap><institution-id institution-id-type="ISNI">0000 0000 9337 0481</institution-id>
<institution-id institution-id-type="GRID">grid.412896.0</institution-id>
<institution>Professional Master Program in Artificial Intelligence in Medicine, Taipei Medical University,</institution>
</institution-wrap>
Keelung Road, Da’an Distric, Taipei City, 106 Taiwan (R.O.C.)</aff>
<aff id="Aff4"><label>4</label>
Institute of Research and Development, Duy Tan University, Danang 550000, Vietnam</aff>
<aff id="Aff5"><label>5</label>
<institution-wrap><institution-id institution-id-type="ISNI">0000 0001 0307 1240</institution-id>
<institution-id institution-id-type="GRID">grid.440588.5</institution-id>
<institution>School of Marine Science and Technology, Northwestern Polytechnical University,</institution>
</institution-wrap>
127 West Youyi Road, Xi’an 710072, China</aff>
</contrib-group>
<pub-date pub-type="epub"><day>24</day>
<month>12</month>
<year>2019</year>
</pub-date>
<pub-date pub-type="pmc-release"><day>24</day>
<month>12</month>
<year>2019</year>
</pub-date>
<pub-date pub-type="collection"><year>2019</year>
</pub-date>
<volume>20</volume>
<issue>Suppl 9</issue>
<elocation-id>951</elocation-id>
<permissions><copyright-statement>© The Author(s) 2019</copyright-statement>
<license license-type="OpenAccess"><license-p><bold>Open Access</bold>
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">http://creativecommons.org/licenses/by/4.0/</ext-link>
), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/publicdomain/zero/1.0/">http://creativecommons.org/publicdomain/zero/1.0/</ext-link>
) applies to the data made available in this article, unless otherwise stated.</license-p>
</license>
</permissions>
<abstract id="Abs1"><sec><title>Background</title>
<p id="Par1">Enhancers are non-coding DNA fragments which are crucial in gene regulation (e.g. transcription and translation). Having high locational variation and free scattering in 98% of non-encoding genomes, enhancer identification is, therefore, more complicated than other genetic factors. To address this biological issue, several in silico studies have been done to identify and classify enhancer sequences among a myriad of DNA sequences using computational advances. Although recent studies have come up with improved performance, shortfalls in these learning models still remain. To overcome limitations of existing learning models, we introduce iEnhancer-ECNN, an efficient prediction framework using one-hot encoding and <italic>k</italic>
-mers for data transformation and ensembles of convolutional neural networks for model construction, to identify enhancers and classify their strength. The benchmark dataset from Liu et al.’s study was used to develop and evaluate the ensemble models. A comparative analysis between iEnhancer-ECNN and existing state-of-the-art methods was done to fairly assess the model performance.</p>
</sec>
<sec><title>Results</title>
<p id="Par2">Our experimental results demonstrates that iEnhancer-ECNN has better performance compared to other state-of-the-art methods using the same dataset. The accuracy of the ensemble model for enhancer identification (layer 1) and enhancer classification (layer 2) are 0.769 and 0.678, respectively. Compared to other related studies, improvements in the Area Under the Receiver Operating Characteristic Curve (AUC), sensitivity, and Matthews’s correlation coefficient (MCC) of our models are remarkable, especially for the model of layer 2 with about 11.0%, 46.5%, and 65.0%, respectively.</p>
</sec>
<sec><title>Conclusions</title>
<p id="Par3">iEnhancer-ECNN outperforms other previously proposed methods with significant improvement in most of the evaluation metrics. Strong growths in the MCC of both layers are highly meaningful in assuring the stability of our models.</p>
</sec>
</abstract>
<kwd-group xml:lang="en"><title>Keywords</title>
<kwd>Enhancer</kwd>
<kwd>Identification</kwd>
<kwd>Classification</kwd>
<kwd>Ensemble</kwd>
<kwd>One-hot encoding</kwd>
<kwd>Convolutional neural network</kwd>
<kwd>Deep learning</kwd>
</kwd-group>
<conference xlink:href="https://incob2019.org/"><conf-name>International Conference on Bioinformatics (InCoB 2019)</conf-name>
<conf-acronym>InCoB 2019</conf-acronym>
<conf-loc>Jakarta, Indonesia</conf-loc>
<conf-date>10-12 September 2019</conf-date>
</conference>
<custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name>
<meta-value>© The Author(s) 2019</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
</front>
</pmc>
</record>
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