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Combinatorial Chemistry & High Throughput Screening

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ISSN (Print): 1386-2073
ISSN (Online): 1875-5402

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Research Article

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

Author(s): Zhongwei Zhao, Xiaoxi Fan, Lili Yang, Jingjing Song, Shiji Fang, Jianfei Tu, Minjiang Chen, Jie Li, Liyun Zheng, Fazong Wu, Dengke Zhang, Xihui Ying and Jiansong Ji*

Volume 22, Issue 4, 2019

Page: [256 - 265] Pages: 10

DOI: 10.2174/1386207322666190530102245

Price: $65

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, lung cancer, related gene pairs, deep learning, clinical diagnosis.

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