Recognition of Lung Adenocarcinoma-specific Gene Pairs Based on Genetic Algorithm and Establishment of a Deep Learning Prediction Model - Zhao, Zhongwei; Fan, Xiaoxi; Yang, Lili; Song, Jingjing; Fang, Shiji; Tu, Jianfei; Chen, Minjiang; Li, Jie; Zheng, Liyun; Wu, Fazong; Zhang, Dengke; Ying, Xihui; et al. (Combinatorial Chemistry & High Throughput Screening, 2019)
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: related gene pairs