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Selection of tumor characteristic genes based on data mining technology

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DOI: 10.23977/phpm.2022.020206 | Downloads: 8 | Views: 659

Author(s)

Mingxi Chen 1, Yunhao Liu 2, Junming Hou 3

Affiliation(s)

1 Shaanxi University of Chinese Medicine, Xixian Avenue, Xixian new area 712046, Shaanxi Province
2 Affiliated Hospital of Shaanxi University of Chinese Medicine, No. 2, Weiyang West Road, Qindu District, Xianyang City 712000, Shaanxi Province, Department of Surgical Thoracic
3 Affiliated Hospital of Shaanxi University of Chinese Medicine, No. 2, Weiyang West Road, Qindu District, Xianyang, Department of Surgical Oncology

Corresponding Author

Junming Hou

ABSTRACT

Gene chip technology is widely used to study gene expression patterns of cells at genome level because it can quickly measure the expression levels of thousands of genes at the same time. Gene microarray technology can track and monitor tens of thousands of gene expression levels in different tissues. It not only provides a powerful scientific basis for cancer biology research, but also helps the classification and identification of cancer tissues. With the wide application of microarray technology in the research of tumor diseases, a large number of tumor gene expression profile data with high dimensions and few samples have been produced. Because of its high efficiency and high throughput, DNA microarray technology has been widely used in various biomedical researches, which can detect a large number of tumor gene expression. Based on data mining technology, today, big data technology has been widely used in all walks of life, which has greatly promoted the development and progress of society. Therefore, in-depth research and discussion on big data technology is of great significance for its future optimization and development.

KEYWORDS

Data mining technology, Tumor characteristics, Gene selection

CITE THIS PAPER

Mingxi Chen, Yunhao Liu, Junming Hou, Selection of tumor characteristic genes based on data mining technology. MEDS Public Health and Preventive Medicine (2022) Vol. 2: 36-41. DOI: http://dx.doi.org/10.23977/phpm.2022.020206.

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