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https://hdl.handle.net/2440/139906
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Type: | Journal article |
Title: | Prediction of Multiple Types of RNA Modifications via Biological Language Model |
Author: | Zhang, Y. Ge, F. Li, F. Yang, X. Song, J. Yu, D.-J. |
Citation: | IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2023; 20(5):3205-3214 |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Issue Date: | 2023 |
ISSN: | 1545-5963 1557-9964 |
Statement of Responsibility: | Ying Zhang, Fang Ge, Fuyi Li, Xibei Yang, Jiangning Song, and Dong-Jun Yu |
Abstract: | It has been demonstrated that RNA modifications play essential roles in multiple biological processes. Accurate identification of RNA modifications in the transcriptome is critical for providing insights into the biological functions and mechanisms. Many tools have been developed for predicting RNA modifications at single-base resolution, which employ conventional feature engineering methods that focus on feature design and feature selection processes that require extensive biological expertise and may introduce redundant information. With the rapid development of artificial intelligence technologies, end-to-end methods are favorably received by researchers. Nevertheless, each well-trained model is only suitable for a specific RNA methylation modification type for nearly all of these approaches. In this study, we present MRM-BERT by feeding task-specific sequences into the powerful BERT (Bidirectional Encoder Representations from Transformers) model and implementing fine-tuning, which exhibits competitive performance to the state-of-the-art methods. MRM-BERT avoids repeated de novo training of the model and can predict multipleRNAmodifications such as pseudouridine, m6A, m5C, and m1A in Mus musculus, Arabidopsis thaliana, and Saccharomyces cerevisiae. In addition, we analyse the attention heads to provide high attention regions for the prediction, and conduct saturated in silico mutagenesis of the input sequences to discover potential changes of RNA modifications, which can better assist researchers in their follow-up research. |
Keywords: | RNA modification; deep learning; self-attention mechanism; BERT; biological language model |
Rights: | © 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information. |
DOI: | 10.1109/tcbb.2023.3283985 |
Grant ID: | http://purl.org/au-research/grants/nhmrc/1127948 http://purl.org/au-research/grants/nhmrc/1144652 http://purl.org/au-research/grants/arc/LP110200333 http://purl.org/au-research/grants/arc/DP120104460 |
Published version: | http://dx.doi.org/10.1109/tcbb.2023.3283985 |
Appears in Collections: | Molecular and Biomedical Science publications |
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