Transboundary Pathogenic microRNA Analysis Framework for Crop Fungi Driven by Biological Big Data and Artificial Intelligence Model.
Identifieur interne : 000060 ( Main/Corpus ); précédent : 000059; suivant : 000061Transboundary Pathogenic microRNA Analysis Framework for Crop Fungi Driven by Biological Big Data and Artificial Intelligence Model.
Auteurs : Tianyue Zhang ; Haowu Chang ; Borui Zhang ; Sifei Liu ; Tianheng Zhao ; Enshuang Zhao ; Hengyi Zhao ; Hao ZhangSource :
- Computational biology and chemistry [ 1476-928X ] ; 2020.
Abstract
Plant fungal diseases have been affecting the world's agricultural production and economic levels for a long time, such as rice blast, gray tomato mold, potato late blight etc. Recent studies have shown that fungal pathogens transmit microRNA as an effector to host plants for infection. However, bioassay-based verification analysis is time-consuming and challenging, and it is difficult to analyze from a global perspective. With the accumulation of fungal and plant-related data, data analysis methods can be used to analyze pathogenic fungal microRNA further. Based on the microRNA expression data of fungal pathogens infecting plants before and after, this paper discusses the selection strategy of sample data, the extraction strategy of pathogenic fungal microRNA, the prediction strategy of a fungal pathogenic microRNA target gene, the bicluster-based fungal pathogenic microRNA functional analysis strategy and experimental verification methods. A general analysis pipeline based on machine learning and bicluster-based function module was proposed for plant-fungal pathogenic microRNA.The pipeline proposed in this paper is applied to the infection process of Magnaporthe oryzae and the infection process of potato late blight. It has been verified to prove the feasibility of the pipeline. It can be extended to other relevant crop pathogen research, providing a new idea for fungal research on plant diseases. It can be used as a reference for understanding the interaction between fungi and plants.
DOI: 10.1016/j.compbiolchem.2020.107401
PubMed: 33068919
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Transboundary Pathogenic microRNA Analysis Framework for Crop Fungi Driven by Biological Big Data and Artificial Intelligence Model.</title><author><name sortKey="Zhang, Tianyue" sort="Zhang, Tianyue" uniqKey="Zhang T" first="Tianyue" last="Zhang">Tianyue Zhang</name><affiliation><nlm:affiliation>College of Computer Science and Technology, Jilin University, China.</nlm:affiliation></affiliation></author><author><name sortKey="Chang, Haowu" sort="Chang, Haowu" uniqKey="Chang H" first="Haowu" last="Chang">Haowu Chang</name><affiliation><nlm:affiliation>College of Computer Science and Technology, Jilin University, China.</nlm:affiliation></affiliation></author><author><name sortKey="Zhang, Borui" sort="Zhang, Borui" uniqKey="Zhang B" first="Borui" last="Zhang">Borui Zhang</name><affiliation><nlm:affiliation>Columbia Independent School, Columbia, MO, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Liu, Sifei" sort="Liu, Sifei" uniqKey="Liu S" first="Sifei" last="Liu">Sifei Liu</name><affiliation><nlm:affiliation>College of Computer Science and Technology, Jilin University, China.</nlm:affiliation></affiliation></author><author><name sortKey="Zhao, Tianheng" sort="Zhao, Tianheng" uniqKey="Zhao T" first="Tianheng" last="Zhao">Tianheng Zhao</name><affiliation><nlm:affiliation>College of Computer Science and Technology, Jilin University, China.</nlm:affiliation></affiliation></author><author><name sortKey="Zhao, Enshuang" sort="Zhao, Enshuang" uniqKey="Zhao E" first="Enshuang" last="Zhao">Enshuang Zhao</name><affiliation><nlm:affiliation>College of Computer Science and Technology, Jilin University, China.</nlm:affiliation></affiliation></author><author><name sortKey="Zhao, Hengyi" sort="Zhao, Hengyi" uniqKey="Zhao H" first="Hengyi" last="Zhao">Hengyi Zhao</name><affiliation><nlm:affiliation>College of Computer Science and Technology, Jilin University, China.</nlm:affiliation></affiliation></author><author><name sortKey="Zhang, Hao" sort="Zhang, Hao" uniqKey="Zhang H" first="Hao" last="Zhang">Hao Zhang</name><affiliation><nlm:affiliation>College of Computer Science and Technology, Jilin University, China. Electronic address: zhangh@jlu.edu.cn.</nlm:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2020">2020</date><idno type="RBID">pubmed:33068919</idno><idno type="pmid">33068919</idno><idno type="doi">10.1016/j.compbiolchem.2020.107401</idno><idno type="wicri:Area/Main/Corpus">000060</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Corpus" wicri:corpus="PubMed">000060</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Transboundary Pathogenic microRNA Analysis Framework for Crop Fungi Driven by Biological Big Data and Artificial Intelligence Model.</title><author><name sortKey="Zhang, Tianyue" sort="Zhang, Tianyue" uniqKey="Zhang T" first="Tianyue" last="Zhang">Tianyue Zhang</name><affiliation><nlm:affiliation>College of Computer Science and Technology, Jilin University, China.</nlm:affiliation></affiliation></author><author><name sortKey="Chang, Haowu" sort="Chang, Haowu" uniqKey="Chang H" first="Haowu" last="Chang">Haowu Chang</name><affiliation><nlm:affiliation>College of Computer Science and Technology, Jilin University, China.</nlm:affiliation></affiliation></author><author><name sortKey="Zhang, Borui" sort="Zhang, Borui" uniqKey="Zhang B" first="Borui" last="Zhang">Borui Zhang</name><affiliation><nlm:affiliation>Columbia Independent School, Columbia, MO, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Liu, Sifei" sort="Liu, Sifei" uniqKey="Liu S" first="Sifei" last="Liu">Sifei Liu</name><affiliation><nlm:affiliation>College of Computer Science and Technology, Jilin University, China.</nlm:affiliation></affiliation></author><author><name sortKey="Zhao, Tianheng" sort="Zhao, Tianheng" uniqKey="Zhao T" first="Tianheng" last="Zhao">Tianheng Zhao</name><affiliation><nlm:affiliation>College of Computer Science and Technology, Jilin University, China.</nlm:affiliation></affiliation></author><author><name sortKey="Zhao, Enshuang" sort="Zhao, Enshuang" uniqKey="Zhao E" first="Enshuang" last="Zhao">Enshuang Zhao</name><affiliation><nlm:affiliation>College of Computer Science and Technology, Jilin University, China.</nlm:affiliation></affiliation></author><author><name sortKey="Zhao, Hengyi" sort="Zhao, Hengyi" uniqKey="Zhao H" first="Hengyi" last="Zhao">Hengyi Zhao</name><affiliation><nlm:affiliation>College of Computer Science and Technology, Jilin University, China.</nlm:affiliation></affiliation></author><author><name sortKey="Zhang, Hao" sort="Zhang, Hao" uniqKey="Zhang H" first="Hao" last="Zhang">Hao Zhang</name><affiliation><nlm:affiliation>College of Computer Science and Technology, Jilin University, China. Electronic address: zhangh@jlu.edu.cn.</nlm:affiliation></affiliation></author></analytic><series><title level="j">Computational biology and chemistry</title><idno type="eISSN">1476-928X</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Plant fungal diseases have been affecting the world's agricultural production and economic levels for a long time, such as rice blast, gray tomato mold, potato late blight etc. Recent studies have shown that fungal pathogens transmit microRNA as an effector to host plants for infection. However, bioassay-based verification analysis is time-consuming and challenging, and it is difficult to analyze from a global perspective. With the accumulation of fungal and plant-related data, data analysis methods can be used to analyze pathogenic fungal microRNA further. Based on the microRNA expression data of fungal pathogens infecting plants before and after, this paper discusses the selection strategy of sample data, the extraction strategy of pathogenic fungal microRNA, the prediction strategy of a fungal pathogenic microRNA target gene, the bicluster-based fungal pathogenic microRNA functional analysis strategy and experimental verification methods. A general analysis pipeline based on machine learning and bicluster-based function module was proposed for plant-fungal pathogenic microRNA.The pipeline proposed in this paper is applied to the infection process of Magnaporthe oryzae and the infection process of potato late blight. It has been verified to prove the feasibility of the pipeline. It can be extended to other relevant crop pathogen research, providing a new idea for fungal research on plant diseases. It can be used as a reference for understanding the interaction between fungi and plants.</div></front></TEI><pubmed><MedlineCitation Status="Publisher" Owner="NLM"><PMID Version="1">33068919</PMID><DateRevised><Year>2020</Year><Month>11</Month><Day>10</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1476-928X</ISSN><JournalIssue CitedMedium="Internet"><Volume>89</Volume><PubDate><Year>2020</Year><Month>Oct</Month><Day>09</Day></PubDate></JournalIssue><Title>Computational biology and chemistry</Title><ISOAbbreviation>Comput Biol Chem</ISOAbbreviation></Journal><ArticleTitle>Transboundary Pathogenic microRNA Analysis Framework for Crop Fungi Driven by Biological Big Data and Artificial Intelligence Model.</ArticleTitle><Pagination><MedlinePgn>107401</MedlinePgn></Pagination><ELocationID EIdType="pii" ValidYN="Y">S1476-9271(20)30810-0</ELocationID><ELocationID EIdType="doi" ValidYN="Y">10.1016/j.compbiolchem.2020.107401</ELocationID><Abstract><AbstractText>Plant fungal diseases have been affecting the world's agricultural production and economic levels for a long time, such as rice blast, gray tomato mold, potato late blight etc. Recent studies have shown that fungal pathogens transmit microRNA as an effector to host plants for infection. However, bioassay-based verification analysis is time-consuming and challenging, and it is difficult to analyze from a global perspective. With the accumulation of fungal and plant-related data, data analysis methods can be used to analyze pathogenic fungal microRNA further. Based on the microRNA expression data of fungal pathogens infecting plants before and after, this paper discusses the selection strategy of sample data, the extraction strategy of pathogenic fungal microRNA, the prediction strategy of a fungal pathogenic microRNA target gene, the bicluster-based fungal pathogenic microRNA functional analysis strategy and experimental verification methods. A general analysis pipeline based on machine learning and bicluster-based function module was proposed for plant-fungal pathogenic microRNA.The pipeline proposed in this paper is applied to the infection process of Magnaporthe oryzae and the infection process of potato late blight. It has been verified to prove the feasibility of the pipeline. It can be extended to other relevant crop pathogen research, providing a new idea for fungal research on plant diseases. It can be used as a reference for understanding the interaction between fungi and plants.</AbstractText><CopyrightInformation>Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Zhang</LastName><ForeName>Tianyue</ForeName><Initials>T</Initials><AffiliationInfo><Affiliation>College of Computer Science and Technology, Jilin University, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Chang</LastName><ForeName>Haowu</ForeName><Initials>H</Initials><AffiliationInfo><Affiliation>College of Computer Science and Technology, Jilin University, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Zhang</LastName><ForeName>Borui</ForeName><Initials>B</Initials><AffiliationInfo><Affiliation>Columbia Independent School, Columbia, MO, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Liu</LastName><ForeName>Sifei</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>College of Computer Science and Technology, Jilin University, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Zhao</LastName><ForeName>Tianheng</ForeName><Initials>T</Initials><AffiliationInfo><Affiliation>College of Computer Science and Technology, Jilin University, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Zhao</LastName><ForeName>Enshuang</ForeName><Initials>E</Initials><AffiliationInfo><Affiliation>College of Computer Science and Technology, Jilin University, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Zhao</LastName><ForeName>Hengyi</ForeName><Initials>H</Initials><AffiliationInfo><Affiliation>College of Computer Science and Technology, Jilin University, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Zhang</LastName><ForeName>Hao</ForeName><Initials>H</Initials><AffiliationInfo><Affiliation>College of Computer Science and Technology, Jilin University, China. Electronic address: zhangh@jlu.edu.cn.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D016454">Review</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>10</Month><Day>09</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Comput Biol Chem</MedlineTA><NlmUniqueID>101157394</NlmUniqueID><ISSNLinking>1476-9271</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Bicluster</Keyword><Keyword MajorTopicYN="N">Differential Expression</Keyword><Keyword MajorTopicYN="N">Random Forest</Keyword><Keyword MajorTopicYN="N">Regulation</Keyword><Keyword MajorTopicYN="N">Target Genes</Keyword><Keyword MajorTopicYN="N">Transboundary</Keyword><Keyword MajorTopicYN="N">microRNA</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2020</Year><Month>06</Month><Day>24</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2020</Year><Month>09</Month><Day>19</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2020</Year><Month>10</Month><Day>05</Day></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>10</Month><Day>18</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>10</Month><Day>18</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>10</Month><Day>17</Day><Hour>20</Hour><Minute>12</Minute></PubMedPubDate></History><PublicationStatus>aheadofprint</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">33068919</ArticleId><ArticleId IdType="pii">S1476-9271(20)30810-0</ArticleId><ArticleId IdType="doi">10.1016/j.compbiolchem.2020.107401</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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