ncDENSE: a novel computational method based on a deep learning framework for non-coding RNAs family prediction
Abstract Background Although research on non-coding RNAs (ncRNAs) is a hot topic in life sciences, the functions of numerous ncRNAs remain unclear. In recent years, researchers have found that ncRNAs of the same family have similar functions, therefore, it is important to accurately predict ncRNAs f...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-02-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-023-05191-6 |
| _version_ | 1797863360386039808 |
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| author | Kai Chen Xiaodong Zhu Jiahao Wang Lei Hao Zhen Liu Yuanning Liu |
| author_facet | Kai Chen Xiaodong Zhu Jiahao Wang Lei Hao Zhen Liu Yuanning Liu |
| author_sort | Kai Chen |
| collection | DOAJ |
| description | Abstract Background Although research on non-coding RNAs (ncRNAs) is a hot topic in life sciences, the functions of numerous ncRNAs remain unclear. In recent years, researchers have found that ncRNAs of the same family have similar functions, therefore, it is important to accurately predict ncRNAs families to identify their functions. There are several methods available to solve the prediction problem of ncRNAs family, whose main ideas can be divided into two categories, including prediction based on the secondary structure features of ncRNAs, and prediction according to sequence features of ncRNAs. The first type of prediction method requires a complicated process and has a low accuracy in obtaining the secondary structure of ncRNAs, while the second type of method has a simple prediction process and a high accuracy, but there is still room for improvement. The existing methods for ncRNAs family prediction are associated with problems such as complicated prediction processes and low accuracy, in this regard, it is necessary to propose a new method to predict the ncRNAs family more perfectly. Results A deep learning model-based method, ncDENSE, was proposed in this study, which predicted ncRNAs families by extracting ncRNAs sequence features. The bases in ncRNAs sequences were encoded by one-hot coding and later fed into an ensemble deep learning model, which contained the dynamic bi-directional gated recurrent unit (Bi-GRU), the dense convolutional network (DenseNet), and the Attention Mechanism (AM). To be specific, dynamic Bi-GRU was used to extract contextual feature information and capture long-term dependencies of ncRNAs sequences. AM was employed to assign different weights to features extracted by Bi-GRU and focused the attention on information with greater weights. Whereas DenseNet was adopted to extract local feature information of ncRNAs sequences and classify them by the full connection layer. According to our results, the ncDENSE method improved the Accuracy, Sensitivity, Precision, F-score, and MCC by 2.08 $$\%$$ % , 2.33 $$\%$$ % , 2.14 $$\%$$ % , 2.16 $$\%$$ % , and 2.39 $$\%$$ % , respectively, compared with the suboptimal method. Conclusions Overall, the ncDENSE method proposed in this paper extracts sequence features of ncRNAs by dynamic Bi-GRU and DenseNet and improves the accuracy in predicting ncRNAs family and other data. |
| first_indexed | 2024-04-09T22:35:26Z |
| format | Article |
| id | doaj.art-d44bcd1e51ab4044af2ab408dda1fca5 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-09T22:35:26Z |
| publishDate | 2023-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-d44bcd1e51ab4044af2ab408dda1fca52023-03-22T12:33:27ZengBMCBMC Bioinformatics1471-21052023-02-0124112010.1186/s12859-023-05191-6ncDENSE: a novel computational method based on a deep learning framework for non-coding RNAs family predictionKai Chen0Xiaodong Zhu1Jiahao Wang2Lei Hao3Zhen Liu4Yuanning Liu5College of Software, Jilin UniversityCollege of Software, Jilin UniversityCollege of Software, Jilin UniversityCollege of Software, Jilin UniversityCollege of Computer Science and Technology, Jilin UniversityCollege of Software, Jilin UniversityAbstract Background Although research on non-coding RNAs (ncRNAs) is a hot topic in life sciences, the functions of numerous ncRNAs remain unclear. In recent years, researchers have found that ncRNAs of the same family have similar functions, therefore, it is important to accurately predict ncRNAs families to identify their functions. There are several methods available to solve the prediction problem of ncRNAs family, whose main ideas can be divided into two categories, including prediction based on the secondary structure features of ncRNAs, and prediction according to sequence features of ncRNAs. The first type of prediction method requires a complicated process and has a low accuracy in obtaining the secondary structure of ncRNAs, while the second type of method has a simple prediction process and a high accuracy, but there is still room for improvement. The existing methods for ncRNAs family prediction are associated with problems such as complicated prediction processes and low accuracy, in this regard, it is necessary to propose a new method to predict the ncRNAs family more perfectly. Results A deep learning model-based method, ncDENSE, was proposed in this study, which predicted ncRNAs families by extracting ncRNAs sequence features. The bases in ncRNAs sequences were encoded by one-hot coding and later fed into an ensemble deep learning model, which contained the dynamic bi-directional gated recurrent unit (Bi-GRU), the dense convolutional network (DenseNet), and the Attention Mechanism (AM). To be specific, dynamic Bi-GRU was used to extract contextual feature information and capture long-term dependencies of ncRNAs sequences. AM was employed to assign different weights to features extracted by Bi-GRU and focused the attention on information with greater weights. Whereas DenseNet was adopted to extract local feature information of ncRNAs sequences and classify them by the full connection layer. According to our results, the ncDENSE method improved the Accuracy, Sensitivity, Precision, F-score, and MCC by 2.08 $$\%$$ % , 2.33 $$\%$$ % , 2.14 $$\%$$ % , 2.16 $$\%$$ % , and 2.39 $$\%$$ % , respectively, compared with the suboptimal method. Conclusions Overall, the ncDENSE method proposed in this paper extracts sequence features of ncRNAs by dynamic Bi-GRU and DenseNet and improves the accuracy in predicting ncRNAs family and other data.https://doi.org/10.1186/s12859-023-05191-6ncRNAs familyDynamic Bi-GRUDenseNetncDENSE |
| spellingShingle | Kai Chen Xiaodong Zhu Jiahao Wang Lei Hao Zhen Liu Yuanning Liu ncDENSE: a novel computational method based on a deep learning framework for non-coding RNAs family prediction BMC Bioinformatics ncRNAs family Dynamic Bi-GRU DenseNet ncDENSE |
| title | ncDENSE: a novel computational method based on a deep learning framework for non-coding RNAs family prediction |
| title_full | ncDENSE: a novel computational method based on a deep learning framework for non-coding RNAs family prediction |
| title_fullStr | ncDENSE: a novel computational method based on a deep learning framework for non-coding RNAs family prediction |
| title_full_unstemmed | ncDENSE: a novel computational method based on a deep learning framework for non-coding RNAs family prediction |
| title_short | ncDENSE: a novel computational method based on a deep learning framework for non-coding RNAs family prediction |
| title_sort | ncdense a novel computational method based on a deep learning framework for non coding rnas family prediction |
| topic | ncRNAs family Dynamic Bi-GRU DenseNet ncDENSE |
| url | https://doi.org/10.1186/s12859-023-05191-6 |
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