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Gene selection and classification for cancer microarray data based on machine learning and similarity measures.

Qingzhong Liu1, Andrew H Sung, Zhongxue Chen

  • 1Department of Computer Science, Sam Houston State University, Huntsville, TX 77341, USA.

BMC Genomics
|February 29, 2012
PubMed
Summary
This summary is machine-generated.

Recursive Feature Addition (RFA) is a novel gene selection method for microarray data analysis. RFA combined with Lagging Prediction Peephole Optimization (LPPO) improves disease classification accuracy and reduces costs.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Microarray data analysis presents challenges due to high dimensionality and small sample sizes.
  • Key issues include effective gene selection for reliable disease prediction and optimal gene set determination for classification.
  • Genetic marker associations offer opportunities to reduce classification costs by exploiting information redundancy.

Purpose of the Study:

  • To propose a novel gene selection method, Recursive Feature Addition (RFA), to address redundant information and enhance classification accuracy.
  • To introduce an algorithm, Lagging Prediction Peephole Optimization (LPPO), for determining the optimal gene set for prediction and classification.
  • To evaluate the performance of RFA and LPPO against existing gene selection techniques using benchmark microarray datasets.

Main Methods:

  • Recursive Feature Addition (RFA): A gene selection method combining supervised learning and statistical similarity measures.
  • Lagging Prediction Peephole Optimization (LPPO): An algorithm designed for identifying the final optimal gene set for classification.
  • Comparative analysis: RFA was benchmarked against methods like Support Vector Machine Recursive Feature Elimination and Leave-One-Out Calculation Sequential Forward Selection on six gene expression datasets.

Main Results:

  • Recursive Feature Addition (RFA) demonstrated superior performance compared to other gene selection methods when used with common learning machines (Nearest Mean Scaled Classifier, Support Vector Machine, Naive Bayes Classifier, Random Forest).
  • Lagging Prediction Peephole Optimization (LPPO) was found to be more effective than random strategies.
  • The combination of RFA and LPPO achieved higher testing accuracies than the varSelRF gene selection method.

Conclusions:

  • Recursive Feature Addition (RFA) is an effective gene selection method for microarray data analysis.
  • The integration of RFA with Lagging Prediction Peephole Optimization (LPPO) enhances predictive accuracy in disease classification.
  • The proposed methods offer a promising approach for cost-effective and accurate gene selection in high-dimensional biological data.