Feature gene selection and functional validation of SH3KBP1 in infantile hemangioma using machine learning.

Journal: Biochemical and biophysical research communications

PMID: 39955954

Abstract

BACKGROUND: Infantile hemangioma (IH) is a prevalent vascular tumor in infancy with a complex pathogenesis that remains unclear. This study aimed to investigate the underlying mechanisms of IH using comprehensive bioinformatics analyses and in vitro experiments.

Authors

Jiu Yin

Suining Central Hospital, Suning, 629000, Sichuan, China. Electronic address: yinjiu110@126.com.
Hui Gou

Suining First People's Hospital, Suning, 629000, Sichuan, China. Electronic address: 17828921683@163.com.
Jian Qi

Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China.
Wenli Xing

Suining Central Hospital, Suning, 629000, Sichuan, China. Electronic address: 19911866140@163.com.

Keywords

Adaptor Proteins, Signal Transducing Child, Preschool Computational Biology Female Gene Expression Profiling Gene Expression Regulation, Neoplastic Gene Regulatory Networks Hemangioma Humans Infant Machine Learning Male MicroRNAs

External Resources

View on PubMed Access via DOI PubMed (39955954)

Feature gene selection and functional validation of SH3KBP1 in infantile hemangioma using machine learning.

Abstract

Authors

Keywords

External Resources

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