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Determination of endometrial carcinoma with gene expression based on optimized Elman neural network

Author

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  • Hu, Hongping
  • Wang, Haiyan
  • Bai, Yanping
  • Liu, Maoxing

Abstract

Endometrial carcinoma is a life-threatening disease that causes serious damage to the women’s health. This paper discusses classifications of 87 endometrial samples with gene expressions that are cancerous or cancer-free. Every sample has 5 indicators. For every indicator, the corresponding genes of the missing data are deleted and the signal noise ratios (SNRs) are calculated to filter the irrelevant genes. Then the obtained new samples use the principle component analysis to decrease the dimensions. Finally 10 random samples are selected to be the testing samples for classification. Thus the classification accuracy rate is given for every indicator. Based on cancer related to 5 indicators, the combination of the 5 indicators is used to classify to make new 87 endometrial samples as cancerous or cancer-free. We repeatedly process these new samples by deleting the missing data, filtering the irrelevant genes with SNRs, and decreasing the dimensions with PCA, an obtain the new data. The proposed method is that the particle swarm algorithm (PSO) and the grey wolf optimizer (GWO) is combined to optimize the parameters of Elman recurrent neural network (ERNN), written as PSOGWO-ERNN. The results show that PSOGWO-ERNN is superior to the single ERNN, ERNN optimized by PSO or GWO (PSO-ERNN or GWO-ERNN), and the classification accuracy rate of PSOGWO-ERNN reaches 88.8506%. The results also show that the neural networks optimized by some swarm intelligence algorithms are more useful for classification.

Suggested Citation

  • Hu, Hongping & Wang, Haiyan & Bai, Yanping & Liu, Maoxing, 2019. "Determination of endometrial carcinoma with gene expression based on optimized Elman neural network," Applied Mathematics and Computation, Elsevier, vol. 341(C), pages 204-214.
    • Handle: RePEc:eee:apmaco:v:341:y:2019:i:c:p:204-214
    DOI: 10.1016/j.amc.2018.09.005
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    References listed on IDEAS

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    1. Li, Chao & Wang, Li & Sun, Shiwen & Xia, Chengyi, 2018. "Identification of influential spreaders based on classified neighbors in real-world complex networks," Applied Mathematics and Computation, Elsevier, vol. 320(C), pages 512-523.
    2. Ruey-Maw Chen & Chuin-Mu Wang, 2011. "Project Scheduling Heuristics-Based Standard PSO for Task-Resource Assignment in Heterogeneous Grid," Abstract and Applied Analysis, Hindawi, vol. 2011, pages 1-20, February.
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    Cited by:

    1. Hongping Hu & Yangyang Li & Yanping Bai & Juping Zhang & Maoxing Liu, 2019. "The Improved Antlion Optimizer and Artificial Neural Network for Chinese Influenza Prediction," Complexity, Hindawi, vol. 2019, pages 1-12, August.

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