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. 2024 May 30;25(1):531.
doi: 10.1186/s12864-024-10439-3.

MFPINC: prediction of plant ncRNAs based on multi-source feature fusion

Zhenjun Nie  1 , Mengqing Gao  1 , Xiu Jin  1   2 , Yuan Rao  1   2 , Xiaodan Zhang  3   4
Affiliations

MFPINC: prediction of plant ncRNAs based on multi-source feature fusion

Zhenjun Nie et al. BMC Genomics. .

Abstract

Non-coding RNAs (ncRNAs) are recognized as pivotal players in the regulation of essential physiological processes such as nutrient homeostasis, development, and stress responses in plants. Common methods for predicting ncRNAs are susceptible to significant effects of experimental conditions and computational methods, resulting in the need for significant investment of time and resources. Therefore, we constructed an ncRNA predictor(MFPINC), to predict potential ncRNA in plants which is based on the PINC tool proposed by our previous studies. Specifically, sequence features were carefully refined using variance thresholding and F-test methods, while deep features were extracted and feature fusion were performed by applying the GRU model. The comprehensive evaluation of multiple standard datasets shows that MFPINC not only achieves more comprehensive and accurate identification of gene sequences, but also significantly improves the expressive and generalization performance of the model, and MFPINC significantly outperforms the existing competing methods in ncRNA identification. In addition, it is worth mentioning that our tool can also be found on Github ( https://github.com/Zhenj-Nie/MFPINC ) the data and source code can also be downloaded for free.

Keywords: Fusion of deep feature and sequence feature; Plants; ncRNA prediction.

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Conflict of interest statement

The authors declare no competing interests

Figures

Fig. 1
Fig. 1
Performance of the model before and after parameter tuning
Fig. 2
Fig. 2
Comparison between the original depth model and machine learning models using three types of depth features(-S represents using the SVM model)
Fig. 3
Fig. 3
A Comparison of three sequence features and three deep features in different machine learning models; (B) Comparison of three sequence features and the original sequence in different machine learning models
Fig. 4
Fig. 4
A Graph showing the accuracy of 100 experiments; (B) Graph showing the average accuracy of every 5th experiment out of 100 experiments
Fig. 5
Fig. 5
Comparison of fusing multiple quantities of depth features with different sequence features
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Fig. 6
Comparison of deep features and 91 sequence features before and after fusion in RFC models
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Fig. 7
Comparison of recognition accuracy before and after feature fusion
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Fig. 8
Compare the recognition accuracy of the other six tools in eight independent test sets
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Fig. 9
The ROC curve of all 7 tools on the 8 species datasets
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Fig. 10
Flowchart of the development of our tool: (A) Dataset; (B) Feature acquisition processes; (C) Feature fusion and model construct
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Fig. 11
A GRU model; (B) Bi-GRU model
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Fig. 12
Deep feature extraction process
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Fig. 13
Detailed process of feature extraction and fusion
See this image and copyright information in PMC

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