Gene expression time series modeling with principal component and neural network

S. I. Ao, Kwok Po NG*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

10 Citations (Scopus)


In this work, gene expression time series models have been constructed by using principal component analysis (PCA) and neural network (NN). The main contribution of this paper is to develop a methodology for modeling numerical gene expression time series. The PCA-NN prediction models are compared with other popular continuous prediction methods. The proposed model can give us the extracted features from the gene expressions time series and the orders of the prediction accuracies. Therefore, the model can help practitioners to gain a better understanding of a cell cycle, and to find the dependency of genes, which is useful for drug discoveries. Based on the results of two public real datasets, the PCA-NN method outperforms the other continuous prediction methods. In the time series model, we adapt Akaike's information criteria (AIC) tests and cross-validation to select a suitable NN model to avoid the overparameterized problem.

Original languageEnglish
Pages (from-to)351-358
Number of pages8
JournalSoft Computing
Issue number4
Publication statusPublished - Feb 2006

Scopus Subject Areas

  • Software
  • Theoretical Computer Science
  • Geometry and Topology

User-Defined Keywords

  • Gene expression
  • Neural network
  • Nonlinear network inference
  • Principal component analysis
  • Time series


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