Quadratic regression analysis for gene discovery and pattern recognition for non-cyclic short time-course microarray experiments

@article{Liu2004QuadraticRA,
  title={Quadratic regression analysis for gene discovery and pattern recognition for non-cyclic short time-course microarray experiments},
  author={Hua Liu and Sergey Tarima and Aaron S. Borders and Thomas V. Getchell and Marilyn L. Getchell and Arnold J. Stromberg},
  journal={BMC Bioinformatics},
  year={2004},
  volume={6},
  pages={106 - 106}
}
Cluster analyses are used to analyze microarray time-course data for gene discovery and pattern recognition. However, in general, these methods do not take advantage of the fact that time is a continuous variable, and existing clustering methods often group biologically unrelated genes together. We propose a quadratic regression method for identification of differentially expressed genes and classification of genes based on their temporal expression profiles for non-cyclic short time-course… CONTINUE READING

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Bootstrapping cluster analysis: assessing the reliability of conclusions from microarray experiments.

Proceedings of the National Academy of Sciences of the United States of America • 2001
View 3 Excerpts
Highly Influenced

Analyzing time series gene expression data

Bioinformatics • 2004
View 2 Excerpts

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