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Projection-Based Neighborhood Non-Negative Matrix Factorization for lncRNA-Protein Interaction Prediction
Many long ncRNAs (lncRNA) make their effort by interacting with the corresponding RNA-binding proteins, and identifying the interactions between lncRNAs and proteins is important to understand the functions of lncRNA. Compared with the time-consuming and laborious experimental methods, more and more...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2019
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6880730/ https://www.ncbi.nlm.nih.gov/pubmed/31824563 http://dx.doi.org/10.3389/fgene.2019.01148 |
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author | Ma, Yingjun He, Tingting Jiang, Xingpeng |
author_facet | Ma, Yingjun He, Tingting Jiang, Xingpeng |
author_sort | Ma, Yingjun |
collection | PubMed |
description | Many long ncRNAs (lncRNA) make their effort by interacting with the corresponding RNA-binding proteins, and identifying the interactions between lncRNAs and proteins is important to understand the functions of lncRNA. Compared with the time-consuming and laborious experimental methods, more and more computational models are proposed to predict lncRNA-protein interactions. However, few models can effectively utilize the biological network topology of lncRNA (protein) and combine its sequence structure features, and most models cannot effectively predict new proteins (lncRNA) that do not interact with any lncRNA (proteins). In this study, we proposed a projection-based neighborhood non-negative matrix decomposition model (PMKDN) to predict potential lncRNA-protein interactions by integrating multiple biological features of lncRNAs (proteins). First, according to lncRNA (protein) sequences and lncRNA expression profile data, we extracted multiple features of lncRNA (protein). Second, based on protein GO ontology annotation, lncRNA sequences, lncRNA(protein) feature information, and modified lncRNA-protein interaction network, we calculated multiple similarities of lncRNA (protein), and fused them to obtain a more accurate lncRNA(protein) similarity network. Finally, combining the similarity and various feature information of lncRNA (protein), as well as the modified interaction network, we proposed a projection-based neighborhood non-negative matrix decomposition algorithm to predict the potential lncRNA-protein interactions. On two benchmark datasets, PMKDN showed better performance than other state-of-the-art methods for the prediction of new lncRNA-protein interactions, new lncRNAs, and new proteins. Case study further indicates that PMKDN can be used as an effective tool for lncRNA-protein interaction prediction. |
format | Online Article Text |
id | pubmed-6880730 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-68807302019-12-10 Projection-Based Neighborhood Non-Negative Matrix Factorization for lncRNA-Protein Interaction Prediction Ma, Yingjun He, Tingting Jiang, Xingpeng Front Genet Genetics Many long ncRNAs (lncRNA) make their effort by interacting with the corresponding RNA-binding proteins, and identifying the interactions between lncRNAs and proteins is important to understand the functions of lncRNA. Compared with the time-consuming and laborious experimental methods, more and more computational models are proposed to predict lncRNA-protein interactions. However, few models can effectively utilize the biological network topology of lncRNA (protein) and combine its sequence structure features, and most models cannot effectively predict new proteins (lncRNA) that do not interact with any lncRNA (proteins). In this study, we proposed a projection-based neighborhood non-negative matrix decomposition model (PMKDN) to predict potential lncRNA-protein interactions by integrating multiple biological features of lncRNAs (proteins). First, according to lncRNA (protein) sequences and lncRNA expression profile data, we extracted multiple features of lncRNA (protein). Second, based on protein GO ontology annotation, lncRNA sequences, lncRNA(protein) feature information, and modified lncRNA-protein interaction network, we calculated multiple similarities of lncRNA (protein), and fused them to obtain a more accurate lncRNA(protein) similarity network. Finally, combining the similarity and various feature information of lncRNA (protein), as well as the modified interaction network, we proposed a projection-based neighborhood non-negative matrix decomposition algorithm to predict the potential lncRNA-protein interactions. On two benchmark datasets, PMKDN showed better performance than other state-of-the-art methods for the prediction of new lncRNA-protein interactions, new lncRNAs, and new proteins. Case study further indicates that PMKDN can be used as an effective tool for lncRNA-protein interaction prediction. Frontiers Media S.A. 2019-11-20 /pmc/articles/PMC6880730/ /pubmed/31824563 http://dx.doi.org/10.3389/fgene.2019.01148 Text en Copyright © 2019 Ma, He and Jiang http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics Ma, Yingjun He, Tingting Jiang, Xingpeng Projection-Based Neighborhood Non-Negative Matrix Factorization for lncRNA-Protein Interaction Prediction |
title | Projection-Based Neighborhood Non-Negative Matrix Factorization for lncRNA-Protein Interaction Prediction |
title_full | Projection-Based Neighborhood Non-Negative Matrix Factorization for lncRNA-Protein Interaction Prediction |
title_fullStr | Projection-Based Neighborhood Non-Negative Matrix Factorization for lncRNA-Protein Interaction Prediction |
title_full_unstemmed | Projection-Based Neighborhood Non-Negative Matrix Factorization for lncRNA-Protein Interaction Prediction |
title_short | Projection-Based Neighborhood Non-Negative Matrix Factorization for lncRNA-Protein Interaction Prediction |
title_sort | projection-based neighborhood non-negative matrix factorization for lncrna-protein interaction prediction |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6880730/ https://www.ncbi.nlm.nih.gov/pubmed/31824563 http://dx.doi.org/10.3389/fgene.2019.01148 |
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