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

Hongping Hu, Haiyan Wang, Yanping Bai, Maoxing Liu

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)204-214
Number of pages11
JournalApplied Mathematics and Computation
StatePublished - Jan 15 2019


  • Elman neural network
  • Endometrial carcinoma
  • Gene expression
  • Grey wolf optimizer
  • Leave-one-out cross validation
  • Particular swarm optimization

ASJC Scopus subject areas

  • Computational Mathematics
  • Applied Mathematics


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