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AltWOA: Altruistic Whale Optimization Algorithm for feature selection on microarray datasets.

Rohit Kundu1, Soham Chattopadhyay1, Erik Cuevas2

  • 1Department of Electrical Engineering, Jadavpur University, Kolkata, 700032, India.

Computers in Biology and Medicine
|March 18, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an Altruistic Whale Optimization Algorithm (AltWOA) for efficient feature selection in high-dimensional gene expression data. AltWOA improves disease biomarker identification by accurately filtering relevant genes.

Keywords:
AltruismCancer detectionEvolutionary meta-heuristicFeature selectionGene expressionMicroarray data

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • High-dimensional biomedical data, such as DNA microarray gene expression datasets, are crucial for identifying disease biomarkers.
  • Feature selection is essential for managing the complexity of these datasets and improving the accuracy of disease identification.

Purpose of the Study:

  • To propose an improved meta-heuristic algorithm, the Altruistic Whale Optimization Algorithm (AltWOA), for effective feature selection in high-dimensional microarray data.
  • To enhance the identification of disease-specific gene expression biomarkers.

Main Methods:

  • Development of the Altruistic Whale Optimization Algorithm (AltWOA), an enhancement of the Whale Optimization Algorithm, incorporating altruism for improved solution propagation.
  • Application and evaluation of AltWOA for feature selection on eight high-dimensional microarray datasets.

Main Results:

  • AltWOA demonstrated superior performance compared to existing popular and classical feature selection techniques.
  • The algorithm achieved higher accuracy and selected a more refined set of relevant features (biomarkers).

Conclusions:

  • The proposed AltWOA is an effective method for feature selection in high-dimensional gene expression data.
  • AltWOA offers a promising approach for improving biomarker discovery and disease identification in bioinformatics.