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  • Meeting abstract
  • Open Access

Fuzzy rule based unsupervised approach for gene saliency

BMC Bioinformatics200910 (Suppl 7) :A2

https://doi.org/10.1186/1471-2105-10-S7-A2

  • Published:

Keywords

  • Microarray Data
  • Fuzzy Rule
  • Classifier Performance
  • Ranking Method
  • Class Information

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.

Table 1

 

Classifier performance

Ranking scheme and genes

SVM

KNN

t-test[2]

0.8571

0.8571

   T64297, T96548, M97496, T71025 and H20709

  

Fuzzy rule based method

1.0000

1.0000

   M94132, X53416, b-actin-M, Z24727 and L08010

  

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.

Declarations

Acknowledgements

This work was partially supported by NIH grant HD052472.

Authors’ Affiliations

(1)
Center for Integrative and Translational Genomics, Department of Molecular Sciences, University of Tennessee, Memphis, TN 38163, USA

References

  1. Notterman Carcinoma Data. [http://microarray.princeton.edu/oncology/carcinoma.html]
  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. 10.1126/science.286.5439.531.View ArticlePubMedGoogle Scholar

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