Non-Codeing RNAs Family Prediction Based on RNA Representation and Deep Learning
Paper
Teragawa, Shoryu, Wang, Lei and Liu, Yi. 2024. "Non-Codeing RNAs Family Prediction Based on RNA Representation and Deep Learning." N., Shen W.Shen W.Barthes J.-P.Luo J.Qiu T.Zhou X.Zhang J.Zhu H.Peng K.Xu T.Chen (ed.) 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD 2024 ). Tianjin, China 08 - 10 May 2024 United States. IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/CSCWD61410.2024.10580496
| Paper/Presentation Title | Non-Codeing RNAs Family Prediction Based on RNA Representation and Deep Learning |
|---|---|
| Presentation Type | Paper |
| Authors | Teragawa, Shoryu, Wang, Lei and Liu, Yi |
| Editors | N., Shen W.Shen W.Barthes J.-P.Luo J.Qiu T.Zhou X.Zhang J.Zhu H.Peng K.Xu T.Chen |
| Journal or Proceedings Title | Proceedings of the 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD 2024) |
| Journal Citation | pp. 3206-3211 |
| Number of Pages | 6 |
| Year | 2024 |
| Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
| Place of Publication | United States |
| ISBN | 9798350349184 |
| Digital Object Identifier (DOI) | https://doi.org/10.1109/CSCWD61410.2024.10580496 |
| Web Address (URL) of Paper | https://ieeexplore.ieee.org/document/10580496 |
| Web Address (URL) of Conference Proceedings | https://ieeexplore.ieee.org/xpl/conhome/10579968/proceeding |
| Conference/Event | 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD 2024 ) |
| Event Details | 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD 2024 ) Parent International Conference on Computer Supported Cooperative Work in Design Delivery In person Event Date 08 to end of 10 May 2024 Event Location Tianjin, China |
| Abstract | Predicting RNAs family presents a significant challenge with broad implications in medicine and scientific inquiry. Leveraging the advancements in deep learning, recent studies have delved into utilizing these algorithms for RNAs family prediction. This article introduces an innovative algorithm integrating BiLSTM, transformer, and convolutional neural networks (CNN). Initially, RNA sequences undergo representation using k-mers, a strategy aimed at mitigating the impact of errors in the sequence modeling process. Following this, a word embedding technique is applied to represent the RNA sequences, thereby reducing computational complexity within the network. Experimental results demonstrate the superior performance of our model compared to other comparison algorithms in terms of recall, accuracy, and precision on the 10-fold test dataset. This demonstrates the excellent comprehensive performance of the proposed model in terms of robustness and efficiency. |
| Keywords | deep learning; non-coding RNAs family; k-mer |
| Contains Sensitive Content | Does not contain sensitive content |
| ANZSRC Field of Research 2020 | 460508. Information retrieval and web search |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | Dalian University of Technology, China |
| School of Engineering |
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