Patterns of Gene Expression Profiles Associated with Colorectal Cancer in Colorectal Mucosa by Using Machine Learning Methods.

Journal: Combinatorial chemistry & high throughput screening

PMID: 37957897

Abstract

BACKGROUND: Colorectal cancer (CRC) has a very high incidence and lethality rate and is one of the most dangerous cancer types. Timely diagnosis can effectively reduce the incidence of colorectal cancer. Changes in para-cancerous tissues may serve as an early signal for tumorigenesis. Comparison of the differences in gene expression between para-cancerous and normal mucosa can help in the diagnosis of CRC and understanding the mechanisms of development.

Authors

Jing Xin Ren

School of Life Sciences, Shanghai University, Shanghai, 200444, China.
Lei Chen

Department of Chemistry, Stony Brook University Stony Brook NY USA.
Wei Guo

Emergency Department, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Kai Yan Feng

Department of Computer Science, Guangdong AIB Polytechnic College, Guangzhou, 510507, China.
Yu-Dong Cai

College of Life Science, Shanghai University, Shanghai, People's Republic of China.
Tao Huang

The Second Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, China.

Keywords

Algorithms Antigens, CD Cadherins Colorectal Neoplasms Gene Expression Profiling Gene Expression Regulation, Neoplastic Humans Intestinal Mucosa Machine Learning Support Vector Machine Transcriptome

External Resources

View on PubMed Access via DOI PubMed (37957897)

Patterns of Gene Expression Profiles Associated with Colorectal Cancer in Colorectal Mucosa by Using Machine Learning Methods.

Abstract

Authors

Keywords

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