Genetic protein sequence analysis based on sequence alignment techniques for time series data

Research Article
Open access

Genetic protein sequence analysis based on sequence alignment techniques for time series data

Shengjia Ni 1*
  • 1 Shenghua Zizhu Academy    
  • *corresponding author yanghua97065@tongji.edu.cn
Published on 22 February 2024 | https://doi.org/10.54254/2755-2721/41/20230709
ACE Vol.41
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-307-4
ISBN (Online): 978-1-83558-308-1

Abstract

This study aims to explore the application and effectiveness of sequence comparison techniques in dealing with missing and outliers in time series data. First, the data are pre-processed by convolutional neural network (CNN) and recurrent neural networks (RNN) to remove noise and outliers. Then, time series data at different time points are compared and analysed using the comparison loss function to identify changes and differences in the data. Finally, the prediction performance of different models is evaluated using a variety of assessment metrics, and the results are compared and analysed to verify the effectiveness of the sequence comparison technique in dealing with missing and outliers. The experimental results show that the sequence comparison technique can effectively deal with missing and outliers in time series data, providing important insights for further research on the application and development of the sequence comparison technique. Future research can explore the application of sequence comparison techniques in more fields to optimize model performance and improve accuracy and stability.

Keywords:

Sequence Comparison Techniques, Convolutional Neural Network, Recurrent Neural Network, Time Series Data

Ni,S. (2024). Genetic protein sequence analysis based on sequence alignment techniques for time series data. Applied and Computational Engineering,41,49-53.
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References

[1]. Harrison X A 2021 A brief introduction to the analysis of time-series data from biologging studies Philos Trans R Soc Lond B Biol Sci, 376(1831): p 20200227

[2]. Long K Cai L He L 2018 DNA Sequencing Data Analysis Methods Mol Biol 1754: pp 1-13

[3]. Stahlschmidt S Ulfenborg B Synnergren J 2022 Multimodal deep learning for biomedical data fusion: a review Brief Bioinform 23(2): p bbab569

[4]. Derry A Krzywinski M Altman N 2023 Convolutional neural networks. Nat Methods 20(9): pp 1269-1270

[5]. Kriegeskorte N Golan T 2019 Neural network models and deep learning Curr Biol 29(7): pp R231-R236

[6]. Animesh C Chandraker M Tuned Contrastive Learning arXiv Preprint. arXiv:2305.10675

[7]. Dataset https://www.kaggle.com/datasets/aliabedimadiseh/grch38-human-genome-dna

[8]. Jin Y Li Z Qin C et al 2023 A novel attentional deep neural network-based assessment method for ECG quality Biomedical signal processing and control

[9]. Matsunaga N Ohtani Y Hirahara T 2013 Loss Function Considering Multiple Attributes of a Temporal Sequence for Feed-Forward Neural Networks. IEICE Transactions on Information and Systems E103.D(12): pp 2659-2672

[10]. Karpenko A P Ovchinnikov V A 2021 How to Trick a Neural Network? Synthesising Noise to Reduce the Accuracy of Neural Network Image Classification. Herald of the Bauman Moscow State Technical University Series Instrument Engineering 1 (134): pp 102-119

Cite this article

Ni,S. (2024). Genetic protein sequence analysis based on sequence alignment techniques for time series data. Applied and Computational Engineering,41,49-53.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation

ISBN：978-1-83558-307-4(Print) / 978-1-83558-308-1(Online)
Editor：Mustafa İSTANBULLU
Conference website: https://2023.confmla.org/
Conference date: 18 October 2023
Series: Applied and Computational Engineering
Volume number: Vol.41
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Harrison X A 2021 A brief introduction to the analysis of time-series data from biologging studies Philos Trans R Soc Lond B Biol Sci, 376(1831): p 20200227

[2]. Long K Cai L He L 2018 DNA Sequencing Data Analysis Methods Mol Biol 1754: pp 1-13

[3]. Stahlschmidt S Ulfenborg B Synnergren J 2022 Multimodal deep learning for biomedical data fusion: a review Brief Bioinform 23(2): p bbab569

[4]. Derry A Krzywinski M Altman N 2023 Convolutional neural networks. Nat Methods 20(9): pp 1269-1270

[5]. Kriegeskorte N Golan T 2019 Neural network models and deep learning Curr Biol 29(7): pp R231-R236

[6]. Animesh C Chandraker M Tuned Contrastive Learning arXiv Preprint. arXiv:2305.10675

[7]. Dataset https://www.kaggle.com/datasets/aliabedimadiseh/grch38-human-genome-dna

[8]. Jin Y Li Z Qin C et al 2023 A novel attentional deep neural network-based assessment method for ECG quality Biomedical signal processing and control

[9]. Matsunaga N Ohtani Y Hirahara T 2013 Loss Function Considering Multiple Attributes of a Temporal Sequence for Feed-Forward Neural Networks. IEICE Transactions on Information and Systems E103.D(12): pp 2659-2672

[10]. Karpenko A P Ovchinnikov V A 2021 How to Trick a Neural Network? Synthesising Noise to Reduce the Accuracy of Neural Network Image Classification. Herald of the Bauman Moscow State Technical University Series Instrument Engineering 1 (134): pp 102-119

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