BGFE: A Deep Learning Model for ncRNA-Protein Interaction Predictions Based on Improved Sequence Information.
Identifieur interne : 000662 ( PubMed/Checkpoint ); précédent : 000661; suivant : 000663BGFE: A Deep Learning Model for ncRNA-Protein Interaction Predictions Based on Improved Sequence Information.
Auteurs : Zhao-Hui Zhan [République populaire de Chine] ; Li-Na Jia [République populaire de Chine] ; Yong Zhou [République populaire de Chine] ; Li-Ping Li [République populaire de Chine] ; Hai-Cheng Yi [République populaire de Chine]Source :
- International journal of molecular sciences [ 1422-0067 ] ; 2019.
Descripteurs français
- KwdFr :
- MESH :
English descriptors
- KwdEn :
- MESH :
- chemical , genetics : RNA, Untranslated.
- methods : Computational Biology.
- Amino Acid Sequence, Databases, Protein, Deep Learning, Position-Specific Scoring Matrices, Protein Binding, ROC Curve, Software.
Abstract
The interactions between ncRNAs and proteins are critical for regulating various cellular processes in organisms, such as gene expression regulations. However, due to limitations, including financial and material consumptions in recent experimental methods for predicting ncRNA and protein interactions, it is essential to propose an innovative and practical approach with convincing performance of prediction accuracy. In this study, based on the protein sequences from a biological perspective, we put forward an effective deep learning method, named BGFE, to predict ncRNA and protein interactions. Protein sequences are represented by bi-gram probability feature extraction method from Position Specific Scoring Matrix (PSSM), and for ncRNA sequences, k-mers sparse matrices are employed to represent them. Furthermore, to extract hidden high-level feature information, a stacked auto-encoder network is employed with the stacked ensemble integration strategy. We evaluate the performance of the proposed method by using three datasets and a five-fold cross-validation after classifying the features through the random forest classifier. The experimental results clearly demonstrate the effectiveness and the prediction accuracy of our approach. In general, the proposed method is helpful for ncRNA and protein interacting predictions and it provides some serviceable guidance in future biological research.
DOI: 10.3390/ijms20040978
PubMed: 30813451
Affiliations:
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TS16170022A3@cumt.edu.cn.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>China University of Mining and Technology, Xuzhou 221116</wicri:regionArea><wicri:noRegion>Xuzhou 221116</wicri:noRegion></affiliation></author><author><name sortKey="Jia, Li Na" sort="Jia, Li Na" uniqKey="Jia L" first="Li-Na" last="Jia">Li-Na Jia</name><affiliation wicri:level="1"><nlm:affiliation>College of Information Science and Engineering, Zaozhuang University, Zaozhuang 277100, Shandong, China. jialina@uzz.edu.cn.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>College of Information Science and Engineering, Zaozhuang University, Zaozhuang 277100, Shandong</wicri:regionArea><wicri:noRegion>Shandong</wicri:noRegion></affiliation></author><author><name sortKey="Zhou, Yong" sort="Zhou, Yong" uniqKey="Zhou Y" first="Yong" last="Zhou">Yong Zhou</name><affiliation wicri:level="1"><nlm:affiliation>China University of Mining and Technology, Xuzhou 221116, China. yzhou@cumt.edu.cn.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>China University of Mining and Technology, Xuzhou 221116</wicri:regionArea><wicri:noRegion>Xuzhou 221116</wicri:noRegion></affiliation></author><author><name sortKey="Li, Li Ping" sort="Li, Li Ping" uniqKey="Li L" first="Li-Ping" last="Li">Li-Ping Li</name><affiliation wicri:level="1"><nlm:affiliation>Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China. Lipingli@ms.xjb.ac.cn.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011</wicri:regionArea><wicri:noRegion>Urumqi 830011</wicri:noRegion></affiliation></author><author><name sortKey="Yi, Hai Cheng" sort="Yi, Hai Cheng" uniqKey="Yi H" first="Hai-Cheng" last="Yi">Hai-Cheng Yi</name><affiliation wicri:level="1"><nlm:affiliation>Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China. yihaicheng17@mails.ucas.ac.cn.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011</wicri:regionArea><wicri:noRegion>Urumqi 830011</wicri:noRegion></affiliation></author></analytic><series><title level="j">International journal of molecular sciences</title><idno type="eISSN">1422-0067</idno><imprint><date when="2019" type="published">2019</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Amino Acid Sequence</term><term>Computational Biology (methods)</term><term>Databases, Protein</term><term>Deep Learning</term><term>Position-Specific Scoring Matrices</term><term>Protein Binding</term><term>RNA, Untranslated (genetics)</term><term>ROC Curve</term><term>Software</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>ARN non traduit (génétique)</term><term>Bases de données de protéines</term><term>Biologie informatique ()</term><term>Courbe ROC</term><term>Liaison aux protéines</term><term>Logiciel</term><term>Matrices de scores</term><term>Séquence d'acides aminés</term></keywords><keywords scheme="MESH" type="chemical" qualifier="genetics" xml:lang="en"><term>RNA, Untranslated</term></keywords><keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>ARN non traduit</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Computational Biology</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Amino Acid Sequence</term><term>Databases, Protein</term><term>Deep Learning</term><term>Position-Specific Scoring Matrices</term><term>Protein Binding</term><term>ROC Curve</term><term>Software</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Bases de données de protéines</term><term>Biologie informatique</term><term>Courbe ROC</term><term>Liaison aux protéines</term><term>Logiciel</term><term>Matrices de scores</term><term>Séquence d'acides aminés</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">The interactions between ncRNAs and proteins are critical for regulating various cellular processes in organisms, such as gene expression regulations. However, due to limitations, including financial and material consumptions in recent experimental methods for predicting ncRNA and protein interactions, it is essential to propose an innovative and practical approach with convincing performance of prediction accuracy. In this study, based on the protein sequences from a biological perspective, we put forward an effective deep learning method, named BGFE, to predict ncRNA and protein interactions. Protein sequences are represented by bi-gram probability feature extraction method from Position Specific Scoring Matrix (PSSM), and for ncRNA sequences, k-mers sparse matrices are employed to represent them. Furthermore, to extract hidden high-level feature information, a stacked auto-encoder network is employed with the stacked ensemble integration strategy. We evaluate the performance of the proposed method by using three datasets and a five-fold cross-validation after classifying the features through the random forest classifier. The experimental results clearly demonstrate the effectiveness and the prediction accuracy of our approach. In general, the proposed method is helpful for ncRNA and protein interacting predictions and it provides some serviceable guidance in future biological research.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">30813451</PMID><DateCompleted><Year>2019</Year><Month>06</Month><Day>07</Day></DateCompleted><DateRevised><Year>2020</Year><Month>02</Month><Day>25</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1422-0067</ISSN><JournalIssue CitedMedium="Internet"><Volume>20</Volume><Issue>4</Issue><PubDate><Year>2019</Year><Month>Feb</Month><Day>23</Day></PubDate></JournalIssue><Title>International journal of molecular sciences</Title><ISOAbbreviation>Int J Mol Sci</ISOAbbreviation></Journal><ArticleTitle>BGFE: A Deep Learning Model for ncRNA-Protein Interaction Predictions Based on Improved Sequence Information.</ArticleTitle><ELocationID EIdType="pii" ValidYN="Y">E978</ELocationID><ELocationID EIdType="doi" ValidYN="Y">10.3390/ijms20040978</ELocationID><Abstract><AbstractText>The interactions between ncRNAs and proteins are critical for regulating various cellular processes in organisms, such as gene expression regulations. However, due to limitations, including financial and material consumptions in recent experimental methods for predicting ncRNA and protein interactions, it is essential to propose an innovative and practical approach with convincing performance of prediction accuracy. In this study, based on the protein sequences from a biological perspective, we put forward an effective deep learning method, named BGFE, to predict ncRNA and protein interactions. Protein sequences are represented by bi-gram probability feature extraction method from Position Specific Scoring Matrix (PSSM), and for ncRNA sequences, k-mers sparse matrices are employed to represent them. Furthermore, to extract hidden high-level feature information, a stacked auto-encoder network is employed with the stacked ensemble integration strategy. We evaluate the performance of the proposed method by using three datasets and a five-fold cross-validation after classifying the features through the random forest classifier. The experimental results clearly demonstrate the effectiveness and the prediction accuracy of our approach. In general, the proposed method is helpful for ncRNA and protein interacting predictions and it provides some serviceable guidance in future biological research.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Zhan</LastName><ForeName>Zhao-Hui</ForeName><Initials>ZH</Initials><AffiliationInfo><Affiliation>China University of Mining and Technology, Xuzhou 221116, China. TS16170022A3@cumt.edu.cn.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Jia</LastName><ForeName>Li-Na</ForeName><Initials>LN</Initials><AffiliationInfo><Affiliation>College of Information Science and Engineering, Zaozhuang University, Zaozhuang 277100, Shandong, China. jialina@uzz.edu.cn.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Zhou</LastName><ForeName>Yong</ForeName><Initials>Y</Initials><AffiliationInfo><Affiliation>China University of Mining and Technology, Xuzhou 221116, China. yzhou@cumt.edu.cn.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Li</LastName><ForeName>Li-Ping</ForeName><Initials>LP</Initials><AffiliationInfo><Affiliation>Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China. 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first="Li-Na" last="Jia">Li-Na Jia</name><name sortKey="Li, Li Ping" sort="Li, Li Ping" uniqKey="Li L" first="Li-Ping" last="Li">Li-Ping Li</name><name sortKey="Yi, Hai Cheng" sort="Yi, Hai Cheng" uniqKey="Yi H" first="Hai-Cheng" last="Yi">Hai-Cheng Yi</name><name sortKey="Zhou, Yong" sort="Zhou, Yong" uniqKey="Zhou Y" first="Yong" last="Zhou">Yong Zhou</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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