Clustering Gene Expression Data for Periodic Genes Based on INMF

  • Nini Rao
  • Simon J. Shepherd
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4115)


In this paper, we have explored the use of improved non – negative matrix factorization (INMF) to analyze gene expression data. Firstly, the mathematical principle of INMF algorithm is analyzed; Secondly, we proposed an INMF - based method for clustering periodic genes, which can provide valuable information for gene network research. Using simulated data, our approach is able to extract periodic genes subsets even when the signal-to-noise ratio is low. Subsequently, our approach is tested by real gene expression datasets from Yeast and is compared with the related other approaches. Our results showed that our scheme is feasible and effective.


Independent Component Analysis Independent Component Analysis Periodic Gene Noise Radio Sine Gene 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Nini Rao
    • 1
  • Simon J. Shepherd
    • 2
  1. 1.School of Life Sciences & TechnologyUniversity of Electronic Science & Technology of ChinaChengduP.R. China
  2. 2.Advanced Signals LaboratorySchool of Engineering, Design & Technology, University of BradfordUK

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