Predicting the interaction biomolecule types for lncRNA: an ensemble deep learning approach
Long noncoding RNAs (lncRNAs) play significant roles in various physiological and pathological processes via their interactions with biomolecules like DNA, RNA and protein. The existing in silico methods used for predicting the functions of lncRNA mainly rely on calculating the similarity of lncRNA...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/160384 |
| _version_ | 1811692946317115392 |
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| author | Zhang, Yu Jia, Cangzhi Kwoh, Chee Keong |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Zhang, Yu Jia, Cangzhi Kwoh, Chee Keong |
| author_sort | Zhang, Yu |
| collection | NTU |
| description | Long noncoding RNAs (lncRNAs) play significant roles in various physiological and pathological processes via their interactions with biomolecules like DNA, RNA and protein. The existing in silico methods used for predicting the functions of lncRNA mainly rely on calculating the similarity of lncRNA or investigating whether an lncRNA can interact with a specific biomolecule or disease. In this work, we explored the functions of lncRNA from a different perspective: we presented a tool for predicting the interaction biomolecule type for a given lncRNA. For this purpose, we first investigated the main molecular mechanisms of the interactions of lncRNA-RNA, lncRNA-protein and lncRNA-DNA. Then, we developed an ensemble deep learning model: lncIBTP (lncRNA Interaction Biomolecule Type Prediction). This model predicted the interactions between lncRNA and different types of biomolecules. On the 5-fold cross-validation, the lncIBTP achieves average values of 0.7042 in accuracy, 0.7903 and 0.6421 in macro-average area under receiver operating characteristic curve and precision-recall curve, respectively, which illustrates the model effectiveness. Besides, based on the analysis of the collected published data and prediction results, we hypothesized that the characteristics of lncRNAs that interacted with DNA may be different from those that interacted with only RNA. |
| first_indexed | 2024-10-01T06:43:52Z |
| format | Journal Article |
| id | ntu-10356/160384 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:43:52Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | ntu-10356/1603842022-07-20T07:24:29Z Predicting the interaction biomolecule types for lncRNA: an ensemble deep learning approach Zhang, Yu Jia, Cangzhi Kwoh, Chee Keong School of Computer Science and Engineering Engineering::Computer science and engineering Long Noncoding RNA Functions Machine Learning Long noncoding RNAs (lncRNAs) play significant roles in various physiological and pathological processes via their interactions with biomolecules like DNA, RNA and protein. The existing in silico methods used for predicting the functions of lncRNA mainly rely on calculating the similarity of lncRNA or investigating whether an lncRNA can interact with a specific biomolecule or disease. In this work, we explored the functions of lncRNA from a different perspective: we presented a tool for predicting the interaction biomolecule type for a given lncRNA. For this purpose, we first investigated the main molecular mechanisms of the interactions of lncRNA-RNA, lncRNA-protein and lncRNA-DNA. Then, we developed an ensemble deep learning model: lncIBTP (lncRNA Interaction Biomolecule Type Prediction). This model predicted the interactions between lncRNA and different types of biomolecules. On the 5-fold cross-validation, the lncIBTP achieves average values of 0.7042 in accuracy, 0.7903 and 0.6421 in macro-average area under receiver operating characteristic curve and precision-recall curve, respectively, which illustrates the model effectiveness. Besides, based on the analysis of the collected published data and prediction results, we hypothesized that the characteristics of lncRNAs that interacted with DNA may be different from those that interacted with only RNA. Fundamental Research Funds for the Central Universities (3132020170, 3132019323), the National Natural Science Foundation of Liaoning Province (20180550307). 2022-07-20T07:24:29Z 2022-07-20T07:24:29Z 2021 Journal Article Zhang, Y., Jia, C. & Kwoh, C. K. (2021). Predicting the interaction biomolecule types for lncRNA: an ensemble deep learning approach. Briefings in Bioinformatics, 22(4), bbaa228-. https://dx.doi.org/10.1093/bib/bbaa228 1467-5463 https://hdl.handle.net/10356/160384 10.1093/bib/bbaa228 33003205 2-s2.0-85112129196 4 22 bbaa228 en Briefings in Bioinformatics © The Author(s) 2020. Published by Oxford University Press. All rights reserved. |
| spellingShingle | Engineering::Computer science and engineering Long Noncoding RNA Functions Machine Learning Zhang, Yu Jia, Cangzhi Kwoh, Chee Keong Predicting the interaction biomolecule types for lncRNA: an ensemble deep learning approach |
| title | Predicting the interaction biomolecule types for lncRNA: an ensemble deep learning approach |
| title_full | Predicting the interaction biomolecule types for lncRNA: an ensemble deep learning approach |
| title_fullStr | Predicting the interaction biomolecule types for lncRNA: an ensemble deep learning approach |
| title_full_unstemmed | Predicting the interaction biomolecule types for lncRNA: an ensemble deep learning approach |
| title_short | Predicting the interaction biomolecule types for lncRNA: an ensemble deep learning approach |
| title_sort | predicting the interaction biomolecule types for lncrna an ensemble deep learning approach |
| topic | Engineering::Computer science and engineering Long Noncoding RNA Functions Machine Learning |
| url | https://hdl.handle.net/10356/160384 |
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