A classification model for lncRNA and mRNA based on k-mers and a convolutional neural network.
Identifieur interne : 000698 ( PubMed/Checkpoint ); précédent : 000697; suivant : 000699A classification model for lncRNA and mRNA based on k-mers and a convolutional neural network.
Auteurs : Jianghui Wen [République populaire de Chine] ; Yeshu Liu [République populaire de Chine] ; Yu Shi [République populaire de Chine] ; Haoran Huang [République populaire de Chine] ; Bing Deng [République populaire de Chine] ; Xinping Xiao [République populaire de Chine]Source :
- BMC bioinformatics [ 1471-2105 ] ; 2019.
Descripteurs français
- KwdFr :
- MESH :
- génétique : ARN long non codant, ARN messager.
- Animaux, Humains, Poulets, Souris.
English descriptors
- KwdEn :
- MESH :
- chemical , genetics : RNA, Long Noncoding, RNA, Messenger.
- Animals, Chickens, Humans, Mice, Neural Networks, Computer.
Abstract
Long-chain non-coding RNA (lncRNA) is closely related to many biological activities. Since its sequence structure is similar to that of messenger RNA (mRNA), it is difficult to distinguish between the two based only on sequence biometrics. Therefore, it is particularly important to construct a model that can effectively identify lncRNA and mRNA.
DOI: 10.1186/s12859-019-3039-3
PubMed: 31519146
Affiliations:
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k-mers and a convolutional neural network.</title><author><name sortKey="Wen, Jianghui" sort="Wen, Jianghui" uniqKey="Wen J" first="Jianghui" last="Wen">Jianghui Wen</name><affiliation wicri:level="1"><nlm:affiliation>School of Science, Wuhan University of Technology, Wuhan, 430070, People's Republic of China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>School of Science, Wuhan University of Technology, Wuhan, 430070</wicri:regionArea><wicri:noRegion>430070</wicri:noRegion></affiliation></author><author><name sortKey="Liu, Yeshu" sort="Liu, Yeshu" uniqKey="Liu Y" first="Yeshu" last="Liu">Yeshu Liu</name><affiliation wicri:level="1"><nlm:affiliation>School of Science, Wuhan University of Technology, Wuhan, 430070, People's Republic of China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>School of Science, Wuhan University of Technology, Wuhan, 430070</wicri:regionArea><wicri:noRegion>430070</wicri:noRegion></affiliation></author><author><name sortKey="Shi, Yu" sort="Shi, Yu" uniqKey="Shi Y" first="Yu" last="Shi">Yu Shi</name><affiliation wicri:level="1"><nlm:affiliation>School of Science, Wuhan University of Technology, Wuhan, 430070, People's Republic of China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>School of Science, Wuhan University of Technology, Wuhan, 430070</wicri:regionArea><wicri:noRegion>430070</wicri:noRegion></affiliation></author><author><name sortKey="Huang, Haoran" sort="Huang, Haoran" uniqKey="Huang H" first="Haoran" last="Huang">Haoran Huang</name><affiliation wicri:level="1"><nlm:affiliation>School of Science, Wuhan University of Technology, Wuhan, 430070, People's Republic of China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>School of Science, Wuhan University of Technology, Wuhan, 430070</wicri:regionArea><wicri:noRegion>430070</wicri:noRegion></affiliation></author><author><name sortKey="Deng, Bing" sort="Deng, Bing" uniqKey="Deng B" first="Bing" last="Deng">Bing Deng</name><affiliation wicri:level="1"><nlm:affiliation>Wuhan Academy of Agricultural Sciences, Wuhan, 430208, People's Republic of China. dengbing0906@163.com.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Wuhan Academy of Agricultural Sciences, Wuhan, 430208</wicri:regionArea><wicri:noRegion>430208</wicri:noRegion></affiliation></author><author><name sortKey="Xiao, Xinping" sort="Xiao, Xinping" uniqKey="Xiao X" first="Xinping" last="Xiao">Xinping Xiao</name><affiliation wicri:level="1"><nlm:affiliation>School of Science, Wuhan University of Technology, Wuhan, 430070, People's Republic of China. xiaoxp@whut.edu.cn.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>School of Science, Wuhan University of Technology, Wuhan, 430070</wicri:regionArea><wicri:noRegion>430070</wicri:noRegion></affiliation></author></analytic><series><title level="j">BMC bioinformatics</title><idno type="eISSN">1471-2105</idno><imprint><date when="2019" type="published">2019</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Animals</term><term>Chickens</term><term>Humans</term><term>Mice</term><term>Neural Networks, Computer</term><term>RNA, Long Noncoding (genetics)</term><term>RNA, Messenger (genetics)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>ARN long non codant (génétique)</term><term>ARN messager (génétique)</term><term>Animaux</term><term>Humains</term><term>Poulets</term><term>Souris</term></keywords><keywords scheme="MESH" type="chemical" qualifier="genetics" xml:lang="en"><term>RNA, Long Noncoding</term><term>RNA, Messenger</term></keywords><keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>ARN long non codant</term><term>ARN messager</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Animals</term><term>Chickens</term><term>Humans</term><term>Mice</term><term>Neural Networks, Computer</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Animaux</term><term>Humains</term><term>Poulets</term><term>Souris</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Long-chain non-coding RNA (lncRNA) is closely related to many biological activities. Since its sequence structure is similar to that of messenger RNA (mRNA), it is difficult to distinguish between the two based only on sequence biometrics. Therefore, it is particularly important to construct a model that can effectively identify lncRNA and mRNA.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" IndexingMethod="Curated" Owner="NLM"><PMID Version="1">31519146</PMID><DateCompleted><Year>2019</Year><Month>11</Month><Day>15</Day></DateCompleted><DateRevised><Year>2020</Year><Month>02</Month><Day>25</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1471-2105</ISSN><JournalIssue CitedMedium="Internet"><Volume>20</Volume><Issue>1</Issue><PubDate><Year>2019</Year><Month>Sep</Month><Day>13</Day></PubDate></JournalIssue><Title>BMC bioinformatics</Title><ISOAbbreviation>BMC Bioinformatics</ISOAbbreviation></Journal><ArticleTitle>A classification model for lncRNA and mRNA based on k-mers and a convolutional neural network.</ArticleTitle><Pagination><MedlinePgn>469</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1186/s12859-019-3039-3</ELocationID><Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">Long-chain non-coding RNA (lncRNA) is closely related to many biological activities. Since its sequence structure is similar to that of messenger RNA (mRNA), it is difficult to distinguish between the two based only on sequence biometrics. Therefore, it is particularly important to construct a model that can effectively identify lncRNA and mRNA.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">First, the difference in the k-mer frequency distribution between lncRNA and mRNA sequences is considered in this paper, and they are transformed into the k-mer frequency matrix. Moreover, k-mers with more species are screened by relative entropy. The classification model of the lncRNA and mRNA sequences is then proposed by inputting the k-mer frequency matrix and training the convolutional neural network. Finally, the optimal k-mer combination of the classification model is determined and compared with other machine learning methods in humans, mice and chickens. The results indicate that the proposed model has the highest classification accuracy. Furthermore, the recognition ability of this model is verified to a single sequence.</AbstractText><AbstractText Label="CONCLUSION" NlmCategory="CONCLUSIONS">We established a classification model for lncRNA and mRNA based on k-mers and the convolutional neural network. The classification accuracy of the model with 1-mers, 2-mers and 3-mers was the highest, with an accuracy of 0.9872 in humans, 0.8797 in mice and 0.9963 in chickens, which is better than those of the random forest, logistic regression, decision tree and support vector machine.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Wen</LastName><ForeName>Jianghui</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>School of Science, Wuhan University of Technology, Wuhan, 430070, People's Republic of China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Liu</LastName><ForeName>Yeshu</ForeName><Initials>Y</Initials><AffiliationInfo><Affiliation>School of Science, Wuhan University of Technology, Wuhan, 430070, People's Republic of China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Shi</LastName><ForeName>Yu</ForeName><Initials>Y</Initials><AffiliationInfo><Affiliation>School of Science, Wuhan University of Technology, Wuhan, 430070, People's Republic of China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Huang</LastName><ForeName>Haoran</ForeName><Initials>H</Initials><AffiliationInfo><Affiliation>School of Science, Wuhan University of Technology, Wuhan, 430070, People's Republic of China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Deng</LastName><ForeName>Bing</ForeName><Initials>B</Initials><AffiliationInfo><Affiliation>Wuhan Academy of Agricultural Sciences, Wuhan, 430208, People's Republic of China. dengbing0906@163.com.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Xiao</LastName><ForeName>Xinping</ForeName><Initials>X</Initials><AffiliationInfo><Affiliation>School of Science, Wuhan University of Technology, Wuhan, 430070, People's Republic of China. xiaoxp@whut.edu.cn.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>61403288</GrantID><Agency>National Natural Science Foundation of China</Agency><Country></Country></Grant><Grant><GrantID>71871174</GrantID><Agency>National Natural Science Foundation of China</Agency><Country></Country></Grant><Grant><GrantID>2019cfb589</GrantID><Agency>Natural Science Foundation of Hubei Province, China</Agency><Country></Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2019</Year><Month>09</Month><Day>13</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>BMC Bioinformatics</MedlineTA><NlmUniqueID>100965194</NlmUniqueID><ISSNLinking>1471-2105</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D062085">RNA, Long Noncoding</NameOfSubstance></Chemical><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D012333">RNA, Messenger</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000818" MajorTopicYN="N">Animals</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D002645" MajorTopicYN="N">Chickens</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D051379" MajorTopicYN="N">Mice</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D016571" MajorTopicYN="Y">Neural Networks, Computer</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D062085" MajorTopicYN="N">RNA, Long Noncoding</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="Y">genetics</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D012333" MajorTopicYN="N">RNA, Messenger</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="Y">genetics</QualifierName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Convolutional neural network</Keyword><Keyword MajorTopicYN="N">K-mers</Keyword><Keyword MajorTopicYN="N">Relative entropy</Keyword><Keyword MajorTopicYN="N">lncRNA</Keyword><Keyword MajorTopicYN="N">mRNA</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2018</Year><Month>12</Month><Day>30</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2019</Year><Month>08</Month><Day>21</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2019</Year><Month>9</Month><Day>15</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2019</Year><Month>9</Month><Day>15</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate 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