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Comb Chem High Throughput Screen. 2019;22(4):256-265. doi: 10.2174/1386207322666190530102245.

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

Author information

1
Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, Lishui 323000, China.
2
Department of Anesthesiology, Zhejiang University Lishui Hospital, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui Central Hospital, Lishui, China.

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.

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