Integration of transcriptomic analysis and multiple machine learning approaches identifies NAFLD progression-specific hub genes to reveal distinct genomic patterns and actionable targets

Integration of transcriptomic analysis and multiple machine learning approaches identifies NAFLD progression-specific hub genes to reveal distinct genomic patterns and actionable targets

Abstract Background Nonalcoholic fatty liver disease (NAFLD) is a leading public health problem worldwide. Approximately one fourth of patients with nonalcoholic fatty liver (NAFL) progress to nonalcoholic steatohepatitis (NASH), an advanced stage of NAFLD. Hence, there is an urgent need to make a b...

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Podrobná bibliografie
Hlavní autoři: Jing Sun, Run Shi, Yang Wu, Yan Lou, Lijuan Nie, Chun Zhang, Yutian Cao, Qianhua Yan, Lifang Ye, Shu Zhang, Xuanbin Wang, Qibiao Wu, Xuehua Jiao, Jiangyi Yu, Zhuyuan Fang, Xiqiao Zhou
Médium: Článek
Jazyk:English
Vydáno: SpringerOpen 2024-03-01
Edice:Journal of Big Data
Témata:
Nonalcoholic fatty liver disease
Drug repositioning
Bioinformatic analysis
Machine learning
Hepatocellular carcinoma
Mutational signatures
On-line přístup:https://doi.org/10.1186/s40537-024-00899-5

Internet

https://doi.org/10.1186/s40537-024-00899-5

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