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Gene selection using hybrid binary black hole algorithm and modified binary particle swarm optimization.

Elnaz Pashaei1, Elham Pashaei1, Nizamettin Aydin1

  • 1Department of Computer Engineering, Yildiz Technical University, Istanbul, Turkey.

Genomics
|April 17, 2018
PubMed
Summary
This summary is machine-generated.

A new hybrid algorithm, Binary Particle Swarm Optimization with Binary Black Hole Algorithm (BPSO-BBHA), enhances gene selection for cancer classification. This method improves accuracy and identifies significant genes more effectively than existing approaches.

Keywords:
Binary black hole algorithmBinary particle swarm optimizationGene expressionGene selectionSparse partial least squares discriminant analysis

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Machine Learning in Oncology

Background:

  • Gene selection is crucial for cancer classification but challenging due to vast search spaces.
  • Existing methods often struggle with efficiency and identifying truly significant genes.
  • Meta-heuristic algorithms offer potential for optimizing complex selection processes.

Purpose of the Study:

  • To develop and evaluate a novel hybrid meta-heuristic algorithm for gene selection in cancer classification.
  • To enhance the exploration and exploitation capabilities of existing algorithms through hybridization.
  • To improve the accuracy and efficiency of identifying relevant genes for cancer subtyping.

Main Methods:

  • Development of a hybrid Binary Black Hole Algorithm (BBHA) embedded within Binary Particle Swarm Optimization (BPSO) (4-2) model.
  • Integration of a Random Forest Recursive Feature Elimination (RF-RFE) pre-filtering technique.
  • Evaluation of Sparse Partial Least Squares Discriminant Analysis (SPLSDA), k-nearest neighbor, and Naive Bayes classifiers using the proposed model.

Main Results:

  • The BPSO (4-2)-BBHA model demonstrated superior performance compared to individual BBHA, BPSO (4-2), and other state-of-the-art methods.
  • The hybrid model showed improved accuracy, faster convergence rates, and better avoidance of local minima.
  • The algorithm successfully identified known biologically and statistically significant genes from clinical microarray datasets.

Conclusions:

  • The proposed BPSO (4-2)-BBHA model is an effective approach for gene selection in cancer classification.
  • Hybrid meta-heuristic algorithms can significantly enhance the performance of feature selection tasks.
  • This method holds promise for improving diagnostic and prognostic tools in oncology by identifying key cancer-related genes.