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, Jiansong Ji*.

Journal Name: Combinatorial Chemistry & High Throughput Screening
Accelerated Technologies for Biotechnology, Bioassays, Medicinal Chemistry and Natural Products Research

Volume 22 , Issue 4 , 2019

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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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Article Details

VOLUME: 22
ISSUE: 4
Year: 2019
Page: [256 - 265]
Pages: 10
DOI: 10.2174/1386207322666190530102245
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