A Gene Feature Extraction Method Based on Across-view Similarity Order Preserving

SU Shuzhi; ZHANG Kaiyu; WANG Ziying; ZHANG Maoyan

doi:10.11999/JEIT211126

Volume 45 Issue 1

Jan. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(1): 317-324

SU Shuzhi, ZHANG Kaiyu, WANG Ziying, ZHANG Maoyan. A Gene Feature Extraction Method Based on Across-view Similarity Order Preserving[J]. Journal of Electronics & Information Technology, 2023, 45(1): 317-324. doi: 10.11999/JEIT211126

Citation:

SU Shuzhi, ZHANG Kaiyu, WANG Ziying, ZHANG Maoyan. A Gene Feature Extraction Method Based on Across-view Similarity Order Preserving[J]. Journal of Electronics & Information Technology, 2023, 45(1): 317-324. doi: 10.11999/JEIT211126

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A Gene Feature Extraction Method Based on Across-view Similarity Order Preserving

doi: 10.11999/JEIT211126

1.
School of Computer Science and Engineering, Anhui University of Science & Technology, Huainan 232001, China
2.
Institute of Energy, Hefei Comprehensive National Science Center, Hefei 230031, China

Funds: The National Natural Science Foundation of China (61806006), China Postdoctoral Science Foundation (2019M660149), The Project of Institute of Energy, Hefei Comprehensive National Science Center (19KZS203), The International Science and Technology Cooperation Project of Key Research and Development Plan in Anhui Province (202004b11020029)

Received Date: 2021-10-14
Accepted Date: 2022-01-12
Rev Recd Date: 2022-01-10

Available Online: 2022-02-02

Publish Date: 2023-01-17

Abstract

Abstract

Gene expression data is usually characterized by high dimension, few samples and uneven classification distribution. How to extract the effective features of gene expression data is a critical problem of gene classification. With the help of correlation analysis theory, the within-view and between-view discrimination sensitive similarity order scatter can be construsted, thus forming a new method of gene feature extraction, namely, Similarity Order Preserving Across-view Correlation Analysis(SOPACA). The proposed method not only maintains the intra-class aggregation and similarity order of features between different views, but also has a large distance between classes. Good experimental results on cancer gene expression datasets demonstrate the effectiveness of the method.
- Gene feature extraction,
- Correlation analysis theory,
- Similarity order preserving,
- Discrimination sensitive,
- Cancer diagnosis

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References(24)

References

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