LSA-ac4C: A hybrid neural network incorporating double-layer LSTM and self-attention mechanism for the prediction of N4-acetylcytidine sites in human mRNA

Lai, Fei-Liao; Gao, Feng

doi:10.1016/j.ijbiomac.2023.126837

Cited by 8 publications

(4 citation statements)

References 46 publications

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“…When encoding sequence features, we aim to preserve as much information as possible from the input sequence while avoiding information loss or redundancy caused by manual feature extraction. To this end, we only considered three encoding schemes: One-hot encoding, NCP encoding ( Chen et al, 2016 ; 2017 ; Nguyen-Vo et al, 2019 ), and Embedding encoding ( Hasan et al, 2020 ; Jin et al, 2022b ; Lai and Gao, 2023 ). One-hot encoding is widely used in machine learning, deep learning, bioinformatics, and other fields due to its simplicity, ease of use, stability, and other advantages.…”

Section: Resultsmentioning

confidence: 99%

“…Predicting ac4C sites in human mRNA is a vital bioinformatics problem, and several methods have been proposed and published, such as PACES( Zhao et al, 2019 ), XG-ac4C ( Alam et al, 2020 ), iRNA-ac4C ( Su et al, 2023 ), DLC-ac4C ( Jia et al, 2023a ), and LSA-ac4C ( Lai and Gao, 2023 ). However, the performance of these methods is hard to compare directly because they use different datasets and evaluation criteria.…”

Section: Resultsmentioning

confidence: 99%

“…Frontiers in Genetics frontiersin.org 2019), and Embedding encoding (Hasan et al, 2020;Jin et al, 2022b;Lai and Gao, 2023). One-hot encoding is widely used in machine learning, deep learning, bioinformatics, and other fields due to its simplicity, ease of use, stability, and other advantages.…”

Section: Comparison Of Using One-hot Encoding Ncp Encoding and Embedd...mentioning

confidence: 99%

“…The predictor was based on a gradient boosting decision tree (GBDT), integrating k-mer nucleotide composition, nucleotide chemical property (NCP), and accumulated nucleotide frequency (ANF) as three feature extraction methods, and surpassed the previous models in identifying ac4C sites. Lai and Gao et al proposed LSA-ac4C ( Lai and Gao, 2023 ), a model that used a two-layer LSTM neural network to learn the dependency relationship of mRNA sequences and used the self-attention mechanism to pay attention to the importance of nucleotides in mRNA sequences. They also trained a transformer-based deep learning model using automated machine learning (AutoML) technology called Auto-ac4C.…”

Section: Introductionmentioning

confidence: 99%

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STM-ac4C: a hybrid model for identification of N4-acetylcytidine (ac4C) in human mRNA based on selective kernel convolution, temporal convolutional network, and multi-head self-attention

Yi,

Zhou,

Deng

2024

Front. Genet.

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N4-acetylcysteine (ac4C) is a chemical modification in mRNAs that alters the structure and function of mRNA by adding an acetyl group to the N4 position of cytosine. Researchers have shown that ac4C is closely associated with the occurrence and development of various cancers. Therefore, accurate prediction of ac4C modification sites on human mRNA is crucial for revealing its role in diseases and developing new diagnostic and therapeutic strategies. However, existing deep learning models still have limitations in prediction accuracy and generalization ability, which restrict their effectiveness in handling complex biological sequence data. This paper introduces a deep learning-based model, STM-ac4C, for predicting ac4C modification sites on human mRNA. The model combines the advantages of selective kernel convolution, temporal convolutional networks, and multi-head self-attention mechanisms to effectively extract and integrate multi-level features of RNA sequences, thereby achieving high-precision prediction of ac4C sites. On the independent test dataset, STM-ac4C showed improvements of 1.81%, 3.5%, and 0.37% in accuracy, Matthews correlation coefficient, and area under the curve, respectively, compared to the existing state-of-the-art technologies. Moreover, its performance on additional balanced and imbalanced datasets also confirmed the model’s robustness and generalization ability. Various experimental results indicate that STM-ac4C outperforms existing methods in predictive performance. In summary, STM-ac4C excels in predicting ac4C modification sites on human mRNA, providing a powerful new tool for a deeper understanding of the biological significance of mRNA modifications and cancer treatment. Additionally, the model reveals key sequence features that influence the prediction of ac4C sites through sequence region impact analysis, offering new perspectives for future research. The source code and experimental data are available at https://github.com/ymy12341/STM-ac4C.

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