Clustering gene expression data for periodic genes based on INMF

Authors:
Nini Rao;Simon J. Shepherd
Affiliations:
School of Life Sciences & Technology, University of Electronic Science & Technology of China, Chengdu, P.R. China;Advanced Signals Laboratory, School of Engineering, Design & Technology, University of Bradford, UK
Venue:
ICIC'06 Proceedings of the 2006 international conference on Computational Intelligence and Bioinformatics - Volume Part III
Year:
2006

Citing 4
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Abstract

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.