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Fuzzy rule based unsupervised approach for gene saliency
BMC Bioinformatics volume 10, Article number: A2 (2009)
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 . 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.
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.
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This work was partially supported by NIH grant HD052472.
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Open Access This article is published under license to BioMed Central Ltd. This is an Open Access article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Verma, N.K., Agrawal, P. & Cui, Y. Fuzzy rule based unsupervised approach for gene saliency. BMC Bioinformatics 10 (Suppl 7), A2 (2009). https://doi.org/10.1186/1471-2105-10-S7-A2
- Microarray Data
- Fuzzy Rule
- Classifier Performance
- Ranking Method
- Class Information