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Related Experiment Videos

Regulatory genes identification within functional genomics experiments for tissue classification into binary classes

Bushra Wazir1, Dost Muhammad Khan2, Umair Khalil2

  • 1Department of Statistics, Abdul Wali Khan University, Mardan, Pakistan.

JPMA. the Journal of the Pakistan Medical Association
|January 21, 2021
PubMed
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This summary is machine-generated.

A novel gene selection method using bagging sub-forest (BSF) improved classification accuracy for tissue samples. This approach effectively identifies informative genes, outperforming existing methods.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Accurate gene selection is crucial for effective tissue classification.
  • High-dimensional genomic data presents challenges like the curse of dimensionality and redundancy.

Purpose of the Study:

  • To develop an informative gene selection method.
  • Enhance tissue classification accuracy by filtering relevant genes.
  • Reduce dimensionality and discard irrelevant or redundant genes.

Main Methods:

  • Introduced a novel gene selection technique: bagging sub-forest (BSF).
  • BSF importance is derived from the standard random forest algorithm.
  • Compared BSF against Wilcoxon, masked painter, and proportional overlapped score (POS) methods on five datasets (Colon, Lymph node breast cancer, Leukaemia, Serrated colorectal carcinomas, Breast Cancer).
Keywords:
Gene selection, classification, random forest, cancer, microarray gene expression

Related Experiment Videos

  • Evaluated performance using random forest (RF) and support vector machine (SVM) classifiers on the top 20 selected genes, measuring classification accuracy, Brier score, and sensitivity.

    • Main Results:

    • The proposed BSF method demonstrated superior performance compared to Wilcoxon, masked painter, and POS.
    • BSF achieved better results across all tested datasets when using both RF and SVM classifiers.
    • The method effectively selected informative genes leading to improved predictive performance.

    Conclusions:

    • The bagging sub-forest (BSF) method offers improved performance in gene selection for tissue classification.
    • BSF enhances classification accuracy, Brier score, and sensitivity.
    • This novel method is suitable for accurate tissue sample classification.