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Gene selection algorithms for microarray data based on least squares support vector machine.

E Ke Tang1, P N Suganthan, Xin Yao

  • 1School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore. tangke@pmail.ntu.edu.sg

BMC Bioinformatics
|March 1, 2006
PubMed
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This study introduces novel gene selection algorithms, Leave-One-Out Calculation Sequential Forward Selection (LOOCSFS) and Gradient-Based Leave-One-Out Gene Selection (GLGS), for microarray data analysis. These methods improve classification accuracy and computational efficiency, especially for large datasets.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Microarray data analysis often involves a large number of genes and a small number of samples.
  • Selecting relevant genes is crucial for effective discriminant analysis.
  • Existing gene selection methods may not be optimal for complex datasets.

Purpose of the Study:

  • To propose novel gene selection methods for microarray data.
  • To introduce a new evaluation criterion, the leave-one-out calculation (LOOC) measure.
  • To develop algorithms that enhance classification accuracy and computational efficiency.

Main Methods:

  • Developed the leave-one-out calculation sequential forward selection (LOOCSFS) algorithm.
  • Proposed the gradient-based leave-one-out gene selection (GLGS) algorithm.

Related Experiment Videos

  • Utilized efficient and exact calculation of leave-one-out cross-validation error for least squares support vector machine (LS-SVM).

    • Main Results:

    • Applied LOOCSFS and GLGS to two microarray datasets.
    • Compared the proposed methods with existing gene selection techniques.
    • Demonstrated that the proposed approaches yield more accurate classification results.

    Conclusions:

    • The proposed gene selection methods provide superior gene subsets for classification.
    • Computational complexity is comparable to existing methods.
    • The GLGS algorithm shows improved scalability for datasets with a very large number of genes.