Skip to content
2000
Volume 22, Issue 4
  • ISSN: 1386-2073
  • E-ISSN: 1875-5402

Abstract

Aim and Objective: Lung cancer is a disease with a dismal prognosis and is the major cause of cancer deaths in many countries. Nonetheless, rapid technological developments in genome science guarantees more effective prevention and treatment strategies. Materials and Methods: In this study, genes were pair-matched and screened for lung adenocarcinomaspecific gene relationships. False positives due to fluctuations in single gene expression were avoided and the stability and accuracy of the results was improved. Results: Finally, a deep learning model was constructed with machine learning algorithm to realize the clinical diagnosis of lung adenocarcinoma in patients. Conclusion: Comparing with the traditional methods which takes ingle gene as a feature, the relative difference between gene pairs is a higher order feature, leverage high-order features to build the model can avoid instability caused by a single gene mutation, making the prediction results more reliable.

Loading

Article metrics loading...

/content/journals/cchts/10.2174/1386207322666190530102245
2019-05-01
2025-10-10
Loading full text...

Full text loading...

/content/journals/cchts/10.2174/1386207322666190530102245
Loading
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test