iEnhancer-ECNN: identifying enhancers and their strength using ensembles of convolutional neural networks.
Identifieur interne : 000319 ( PubMed/Curation ); précédent : 000318; suivant : 000320iEnhancer-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 k-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.
DOI: 10.1186/s12864-019-6336-3
PubMed: 31874637
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Nguyen Quoc Khanh Le<affiliation><nlm:affiliation>Professional Master Program in Artificial Intelligence in Medicine, Taipei Medical University, Keelung Road, Da'an Distric, Taipei City, 106, Taiwan (R.O.C.).</nlm:affiliation><wicri:noCountry code="subField">Taiwan (R.O.C.).</wicri:noCountry></affiliation>Le document en format XML
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Nguyen</name><affiliation wicri:level="1"><nlm:affiliation>School of Information and Communication Technology, Hanoi University of Science and Technology, 1 Dai Co Viet, Hanoi 100000, Vietnam.</nlm:affiliation><country xml:lang="fr">Viêt Nam</country><wicri:regionArea>School of Information and Communication Technology, Hanoi University of Science and Technology, 1 Dai Co Viet, Hanoi 100000</wicri:regionArea></affiliation></author><author><name sortKey="Nguyen Vo, Thanh Hoang" sort="Nguyen Vo, Thanh Hoang" uniqKey="Nguyen Vo T" first="Thanh-Hoang" last="Nguyen-Vo">Thanh-Hoang Nguyen-Vo</name><affiliation wicri:level="1"><nlm:affiliation>School of Mathematics and Statistics, Victoria University of Wellington, Gate 7, Kelburn Parade, Wellington, 6142, New Zealand.</nlm:affiliation><country xml:lang="fr">Nouvelle-Zélande</country><wicri:regionArea>School of Mathematics and Statistics, Victoria University of Wellington, Gate 7, Kelburn Parade, Wellington, 6142</wicri:regionArea></affiliation></author><author><name sortKey="Le, Nguyen Quoc Khanh" sort="Le, Nguyen Quoc Khanh" uniqKey="Le N" first="Nguyen Quoc Khanh" last="Le">Nguyen Quoc Khanh Le</name><affiliation><nlm:affiliation>Professional Master Program in Artificial Intelligence in Medicine, Taipei Medical University, Keelung Road, Da'an Distric, Taipei City, 106, Taiwan (R.O.C.).</nlm:affiliation><wicri:noCountry code="subField">Taiwan (R.O.C.).</wicri:noCountry></affiliation></author><author><name sortKey="Do, Trang T T" sort="Do, Trang T T" uniqKey="Do T" first="Trang T T" last="Do">Trang T T. 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Nguyen</name><affiliation wicri:level="1"><nlm:affiliation>School of Information and Communication Technology, Hanoi University of Science and Technology, 1 Dai Co Viet, Hanoi 100000, Vietnam.</nlm:affiliation><country xml:lang="fr">Viêt Nam</country><wicri:regionArea>School of Information and Communication Technology, Hanoi University of Science and Technology, 1 Dai Co Viet, Hanoi 100000</wicri:regionArea></affiliation></author><author><name sortKey="Nguyen Vo, Thanh Hoang" sort="Nguyen Vo, Thanh Hoang" uniqKey="Nguyen Vo T" first="Thanh-Hoang" last="Nguyen-Vo">Thanh-Hoang Nguyen-Vo</name><affiliation wicri:level="1"><nlm:affiliation>School of Mathematics and Statistics, Victoria University of Wellington, Gate 7, Kelburn Parade, Wellington, 6142, New Zealand.</nlm:affiliation><country xml:lang="fr">Nouvelle-Zélande</country><wicri:regionArea>School of Mathematics and Statistics, Victoria University of Wellington, Gate 7, Kelburn Parade, Wellington, 6142</wicri:regionArea></affiliation></author><author><name sortKey="Le, Nguyen Quoc Khanh" sort="Le, Nguyen Quoc Khanh" uniqKey="Le N" first="Nguyen Quoc Khanh" last="Le">Nguyen Quoc Khanh Le</name><affiliation><nlm:affiliation>Professional Master Program in Artificial Intelligence in Medicine, Taipei Medical University, Keelung Road, Da'an Distric, Taipei City, 106, Taiwan (R.O.C.).</nlm:affiliation><wicri:noCountry code="subField">Taiwan (R.O.C.).</wicri:noCountry></affiliation></author><author><name sortKey="Do, Trang T T" sort="Do, Trang T T" uniqKey="Do T" first="Trang T T" last="Do">Trang T T. Do</name><affiliation wicri:level="1"><nlm:affiliation>Institute of Research and Development, Duy Tan University, Danang 550000, Vietnam.</nlm:affiliation><country xml:lang="fr">Viêt Nam</country><wicri:regionArea>Institute of Research and Development, Duy Tan University, Danang 550000</wicri:regionArea></affiliation></author><author><name sortKey="Rahardja, Susanto" sort="Rahardja, Susanto" uniqKey="Rahardja S" first="Susanto" last="Rahardja">Susanto Rahardja</name><affiliation wicri:level="1"><nlm:affiliation>School of Marine Science and Technology, Northwestern Polytechnical University, 127 West Youyi Road, Xi'an 710072, China. susantorahardja@ieee.org.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>School of Marine Science and Technology, Northwestern Polytechnical University, 127 West Youyi Road, Xi'an 710072</wicri:regionArea></affiliation></author><author><name sortKey="Nguyen, Binh P" sort="Nguyen, Binh P" uniqKey="Nguyen B" first="Binh P" last="Nguyen">Binh P. Nguyen</name><affiliation wicri:level="1"><nlm:affiliation>School of Mathematics and Statistics, Victoria University of Wellington, Gate 7, Kelburn Parade, Wellington, 6142, New Zealand. binh.p.nguyen@vuw.ac.nz.</nlm:affiliation><country xml:lang="fr">Nouvelle-Zélande</country><wicri:regionArea>School of Mathematics and Statistics, Victoria University of Wellington, Gate 7, Kelburn Parade, Wellington, 6142</wicri:regionArea></affiliation></author></analytic><series><title level="j">BMC genomics</title><idno type="eISSN">1471-2164</idno><imprint><date when="2019" type="published">2019</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">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 k-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.</div></front></TEI><pubmed><MedlineCitation Status="In-Process" Owner="NLM"><PMID Version="1">31874637</PMID><DateRevised><Year>2020</Year><Month>03</Month><Day>20</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1471-2164</ISSN><JournalIssue CitedMedium="Internet"><Volume>20</Volume><Issue>Suppl 9</Issue><PubDate><Year>2019</Year><Month>Dec</Month><Day>24</Day></PubDate></JournalIssue><Title>BMC genomics</Title><ISOAbbreviation>BMC Genomics</ISOAbbreviation></Journal><ArticleTitle>iEnhancer-ECNN: identifying enhancers and their strength using ensembles of convolutional neural networks.</ArticleTitle><Pagination><MedlinePgn>951</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1186/s12864-019-6336-3</ELocationID><Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">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 k-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.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">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.</AbstractText><AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">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.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Nguyen</LastName><ForeName>Quang H</ForeName><Initials>QH</Initials><AffiliationInfo><Affiliation>School of Information and Communication Technology, Hanoi University of Science and Technology, 1 Dai Co Viet, Hanoi 100000, Vietnam.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Nguyen-Vo</LastName><ForeName>Thanh-Hoang</ForeName><Initials>TH</Initials><AffiliationInfo><Affiliation>School of Mathematics and Statistics, Victoria University of Wellington, Gate 7, Kelburn Parade, Wellington, 6142, New Zealand.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Le</LastName><ForeName>Nguyen Quoc Khanh</ForeName><Initials>NQK</Initials><AffiliationInfo><Affiliation>Professional Master Program in Artificial Intelligence in Medicine, Taipei Medical University, Keelung Road, Da'an Distric, Taipei City, 106, Taiwan (R.O.C.).</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Do</LastName><ForeName>Trang T T</ForeName><Initials>TTT</Initials><AffiliationInfo><Affiliation>Institute of Research and Development, Duy Tan University, Danang 550000, Vietnam.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Rahardja</LastName><ForeName>Susanto</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>School of Marine Science and Technology, Northwestern Polytechnical University, 127 West Youyi Road, Xi'an 710072, China. susantorahardja@ieee.org.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Nguyen</LastName><ForeName>Binh P</ForeName><Initials>BP</Initials><AffiliationInfo><Affiliation>School of Mathematics and Statistics, Victoria University of Wellington, Gate 7, Kelburn Parade, Wellington, 6142, New Zealand. binh.p.nguyen@vuw.ac.nz.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2019</Year><Month>12</Month><Day>24</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>BMC Genomics</MedlineTA><NlmUniqueID>100965258</NlmUniqueID><ISSNLinking>1471-2164</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Classification</Keyword><Keyword MajorTopicYN="N">Convolutional neural network</Keyword><Keyword MajorTopicYN="N">Deep learning</Keyword><Keyword MajorTopicYN="N">Enhancer</Keyword><Keyword MajorTopicYN="N">Ensemble</Keyword><Keyword MajorTopicYN="N">Identification</Keyword><Keyword MajorTopicYN="N">One-hot encoding</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2019</Year><Month>12</Month><Day>26</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2019</Year><Month>12</Month><Day>26</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2019</Year><Month>12</Month><Day>26</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>epublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">31874637</ArticleId><ArticleId IdType="doi">10.1186/s12864-019-6336-3</ArticleId><ArticleId IdType="pii">10.1186/s12864-019-6336-3</ArticleId><ArticleId IdType="pmc">PMC6929481</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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