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

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Abstract

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.
In this study, genes were pair-matched and screened for lung adenocarcinoma-specific gene relationships. False positives due to fluctuations in single gene expression were avoided and the stability and accuracy of the results was improved.
Finally, a deep learning model was constructed with machine learning algorithm to realize the clinical diagnosis of lung adenocarcinoma in patients.
We construct a diagnostic classification model that is capable to accurately predict normal samples and disease samples. 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.
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