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A Novel Gene Selection Algorithm based on Sparse Representation and Minimum-redundancy Maximum-relevancy of Maximum Compatibility Center

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Background: Tumor classification is important for accurate diagnosis and personalized treatment and has recently received great attention. Analysis of gene expression profile has shown relevant biological significance and thus has become a research hotspot and a new challenge for bio-data mining. In the research methods, some algorithms can identify few genes but with great time complexity, some algorithms can get small time complex methods but with unsatisfactory classification accuracy, this article proposed a new extraction method for gene expression profile.

Methods: In this paper, we propose a classification method for tumor subtypes based on the Minimum- Redundancy Maximum-Relevancy (MRMR) of maximum compatibility center. First, we performed a fuzzy clustering of gene expression profiles based on the compatibility relation. Next, we used the sparse representation coefficient to assess the importance of the gene for the category, extracted the top-ranked genes, and removed the uncorrelated genes. Finally, the MRMR search strategy was used to select the characteristic gene, reject the redundant gene, and obtain the final subset of characteristic genes.

Results: Our method and four others were tested on four different datasets to verify its effectiveness. Results show that the classification accuracy and standard deviation of our method are better than those of other methods.

Conclusion: Our proposed method is robust, adaptable, and superior in classification. This method can help us discover the susceptibility genes associated with complex diseases and understand the interaction between these genes. Our technique provides a new way of thinking and is important to understand the pathogenesis of complex diseases and prevent diseases, diagnosis and treatment.
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Keywords: Algorithm; accuracy; bioinformatics; biomarkers; spectrum; tumorigenesis

Document Type: Research Article

Publication date: October 1, 2019

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