This article is part of the supplement: UT-ORNL-KBRIN Bioinformatics Summit 2009 .

Meeting abstract

Fuzzy rule based unsupervised approach for gene saliency

Nishchal K Verma , Pooja Agrawal and Yan Cui

Center for Integrative and Translational Genomics, Department of Molecular Sciences, University of Tennessee, Memphis TN, 38163, USA

author email corresponding author email

from UT-ORNL-KBRIN Bioinformatics Summit 2009
Pikeville, TN, USA. 20–22 March 2009

BMC Bioinformatics 2009, 10(Suppl 7):A2doi:10.1186/1471-2105-10-S7-A2

The electronic version of this abstract is the complete one and can be found online at: http://www.biomedcentral.com/1471-2105/10/S7/A2

Published:

25 June 2009

© 2009 Verma et al; licensee BioMed Central Ltd.

Clinical background

This abstract presents a novel fuzzy rule based gene ranking algorithm for extracting salient genes from a large set of microarray data which helps us to reduce computational efforts towards model building process. The proposed algorithm is an unsupervised approach and does not require any prior class information for gene ranking and microarray data has been used to form a set of robust fuzzy rule base which helps us to find salient genes based on its average relevance with already formed fuzzy rules in rule base. Fuzzy rule based ranking has been carried out to select salient genes based on their average firing strength (i.e. average true value after all the fuzzy rules applied) in order of high relevancy and only top ranked genes are utilized to classify normal and cancerous tissues for a carcinoma dataset [1]. Results validate the effectiveness of our gene ranking method as for the same no. of genes, our ranking scheme helps to improve the classifier performance by selecting better salient genes. In our case study the performance comparison for five top ranked genes is given in Table 1.

Conclusion

Results of classifiers in terms of correct rate (Table 1) show that the proposed fuzzy rule based gene ranking scheme outperforms t-test based ranking schemes.

Acknowledgements

This work was partially supported by NIH grant HD052472.

References

1. Notterman Carcinoma Data [http://microarray.princeton.edu/oncology/carcinoma.html] webcite

   

2. Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Loh ML, Downing JR, Caligiuri MA, Bloomfield CD, Lander ES: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.

   Science 1999, 286:531-537. PubMed Abstract | Publisher Full Text

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