典型文献
A Feature Extraction Method for scRNA-seq Processing and Its Application on COVID-19 Data Analysis
文献摘要：
Single-cell RNA-sequencing (scRNA-seq) is a rapidly increasing research area in biomed-ical 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 hier-archical 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.
文献关键词：
中图分类号：
作者姓名：
Xiumin Shi;Xiyuan Wu;Hengyu Qin
作者机构：
School of Information and Electronics, Beijing Institute of Technol-ogy, Beijing 100081, China
文献出处：
引用格式：
[1]Xiumin Shi;Xiyuan Wu;Hengyu Qin-.A Feature Extraction Method for scRNA-seq Processing and Its Application on COVID-19 Data Analysis)[J].北京理工大学学报（英文版）,2022(03):285-292
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B类：
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AB值：
0.613043
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