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Volume 31Issue 3
Jun. 2022
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Article Navigation> JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY> 2022> 31(3): 285-292
Xiumin Shi, Xiyuan Wu, Hengyu Qin. A Feature Extraction Method for scRNA-seq Processing and Its Application on COVID-19 Data Analysis[J]. JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2022, 31(3): 285-292. doi: 10.15918/j.jbit1004-0579.2022.052
Citation: Xiumin Shi, Xiyuan Wu, Hengyu Qin. A Feature Extraction Method for scRNA-seq Processing and Its Application on COVID-19 Data Analysis[J].JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2022, 31(3): 285-292.doi:10.15918/j.jbit1004-0579.2022.052
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A Feature Extraction Method for scRNA-seq Processing and Its Application on COVID-19 Data Analysis

doi:10.15918/j.jbit1004-0579.2022.052

  • School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China

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  • Author Bio:

    Xiumin Shireceived his Ph.D. degree from Beijing Institute of Technology, China in 2012. From 2015 to 2016, he was a visiting scholar at Stanford University, USA. His current research interests include computational neuroscience, machine learning, computer-aided drug design and bioinformatics, etc

    Xiyuan Wureceived the B.S. degree from Communication University of China, China, in 2019. He is currently pursuing the master’s degree with the School of Information and Electronics, Beijing Institute of Technology, China. His research interests include machine learning, natural language processing and bioinformatics

    Hengyu Qinreceived the B.S. degree from Communication University of China, China, in 2021. He is currently pursuing the master’s degree with the School of Information and Electronics, Beijing Institute of Technology, China. His research interests include machine learning and single-cell omics

  • Corresponding author:sxm@bit.edu.cn
  • Received Date:2022-04-29
  • Rev Recd Date:2022-05-06
  • Accepted Date:2022-05-28
  • Publish Date:2022-06-28
    Abstract
  • Single-cell RNA-sequencing (scRNA-seq) is a rapidly increasing research area in biomedical signal processing. However, the high complexity of single-cell data makes efficient and accurate analysis difficult. To improve the performance of single-cell RNA data processing, two single-cell features calculation method and corresponding dual-input neural network structures are proposed. In this feature extraction and fusion scheme, the features at the cluster level are extracted by hierarchical clustering and differential gene analysis, and the features at the cell level are extracted by the calculation of gene frequency and cross cell frequency. Our experiments on COVID-19 data demonstrate that the combined use of these two feature achieves great results and high robustness for classification tasks.
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