BGFE: A Deep Learning Model for ncRNA-Protein Interaction Predictions Based on Improved Sequence Information
Identifieur interne : 000689 ( Main/Exploration ); précédent : 000688; suivant : 000690BGFE: A Deep Learning Model for ncRNA-Protein Interaction Predictions Based on Improved Sequence Information
Auteurs : Zhao-Hui Zhan ; Li-Na Jia [République populaire de Chine] ; Yong Zhou ; Li-Ping Li ; Hai-Cheng YiSource :
- 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.
Url:
DOI: 10.3390/ijms20040978
PubMed: 30813451
PubMed Central: 6412311
Affiliations:
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Sequence Information</title><author><name sortKey="Zhan, Zhao Hui" sort="Zhan, Zhao Hui" uniqKey="Zhan Z" first="Zhao-Hui" last="Zhan">Zhao-Hui Zhan</name><affiliation><nlm:aff id="af1-ijms-20-00978">China University of Mining and Technology, Xuzhou 221116, China;<email>TS16170022A3@cumt.edu.cn</email> (Z.-H.Z.);<email>yzhou@cumt.edu.cn</email> (Y.Z.)</nlm:aff></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:aff id="af2-ijms-20-00978">College of Information Science and Engineering, Zaozhuang University, Zaozhuang 277100, Shandong, China</nlm:aff><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><nlm:aff id="af1-ijms-20-00978">China University of Mining and Technology, Xuzhou 221116, China;<email>TS16170022A3@cumt.edu.cn</email> (Z.-H.Z.);<email>yzhou@cumt.edu.cn</email> (Y.Z.)</nlm:aff></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><nlm:aff id="af3-ijms-20-00978">Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China;<email>Lipingli@ms.xjb.ac.cn</email> (L.-P.L.);<email>yihaicheng17@mails.ucas.ac.cn</email> (H.-C.Y.)</nlm:aff></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><nlm:aff id="af3-ijms-20-00978">Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China;<email>Lipingli@ms.xjb.ac.cn</email> (L.-P.L.);<email>yihaicheng17@mails.ucas.ac.cn</email> (H.-C.Y.)</nlm:aff></affiliation></author></analytic><series><title level="j">International Journal of Molecular Sciences</title><idno type="eISSN">1422-0067</idno><imprint><date when="2019">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"><p>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.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Harrow, J" uniqKey="Harrow J">J. Harrow</name></author><author><name sortKey="Frankish, A" uniqKey="Frankish A">A. Frankish</name></author><author><name sortKey="Gonzalez, J M" uniqKey="Gonzalez J">J.M. Gonzalez</name></author><author><name sortKey="Tapanari, E" uniqKey="Tapanari E">E. Tapanari</name></author><author><name sortKey="Diekhans, M" uniqKey="Diekhans M">M. Diekhans</name></author><author><name sortKey="Kokocinski, F" uniqKey="Kokocinski F">F. Kokocinski</name></author><author><name sortKey="Aken, B L" uniqKey="Aken B">B.L. Aken</name></author><author><name sortKey="Barrell, D" uniqKey="Barrell D">D. 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Matthews</name></author></analytic></biblStruct></listBibl></div1></back></TEI><affiliations><list><country><li>République populaire de Chine</li></country></list><tree><noCountry><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="Zhan, Zhao Hui" sort="Zhan, Zhao Hui" uniqKey="Zhan Z" first="Zhao-Hui" last="Zhan">Zhao-Hui Zhan</name><name sortKey="Zhou, Yong" sort="Zhou, Yong" uniqKey="Zhou Y" first="Yong" last="Zhou">Yong Zhou</name></noCountry><country name="République populaire de Chine"><noRegion><name sortKey="Jia, Li Na" sort="Jia, Li Na" uniqKey="Jia L" first="Li-Na" last="Jia">Li-Na Jia</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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