A neural network model to screen feature genes for pancreatic cancer

BMC Bioinformatics

Research

Jing Huang¹  Yiming Wu¹  Haoran Zhang¹  Yuting Zhou² 

[1] Department of Gastroenterology, First Hospital of Jiaxing, 314001, Jiaxing, Zhejiang, China;Department of Respiratory, The 904Th Hospital of Joint Logistic Support Force of PLA, Affiliated Hospital of Jiangnan University, 214000, Wuxi, Jiangsu, China;

关键词: Pancreatic cancer;    Neural network model;    Biomarkers;    Gene expression profiling;    Random forest;   

DOI  :  10.1186/s12859-023-05322-z

 received in 2022-12-09, accepted in 2023-05-05,  发布年份 2023

来源: Springer

【 摘 要 】

All the time, pancreatic cancer is a problem worldwide because of its high degree of malignancy and increased mortality. Neural network model analysis is an efficient and accurate machine learning method that can quickly and accurately predict disease feature genes. The aim of our research was to build a neural network model that would help screen out feature genes for pancreatic cancer diagnosis and prediction of prognosis. Our study confirmed that the neural network model is a reliable way to predict feature genes of pancreatic cancer, and immune cells infiltrating play an essential role in the development of pancreatic cancer, especially neutrophils. ANO1, AHNAK2, and ADAM9 were eventually identified as feature genes of pancreatic cancer, helping to diagnose and predict prognosis. Neural network model analysis provides us with a new idea for finding new intervention targets for pancreatic cancer.

【 授权许可】

CC BY   
© The Author(s) 2023

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