Recognition of Lung Adenocarcinoma-specific Gene Pairs Based on Genetic Algorithm and Establishment of a Deep Learning Prediction Model

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.

Keywords: Genetic algorithm; adenocarcinoma; clinical diagnosis; deep learning; lung cancer; related gene pairs..