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Related Concept Videos

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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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A novel bio-inspired hybrid multi-filter wrapper gene selection method with ensemble classifier for microarray data.

Babak Nouri-Moghaddam1, Mehdi Ghazanfari1, Mohammad Fathian1

  • 11684613114 Tehran, Iran Department of Industrial Engineering, Iran University of Science and Technology.

Neural Computing & Applications
|September 20, 2021
PubMed
Summary
This summary is machine-generated.

A novel hybrid approach using an ensemble filter and adaptive chaotic multi-objective forest optimization algorithm (AC-MOFOA) enhances gene selection for improved microarray data classification. This method effectively reduces dimensions and boosts classification accuracy, outperforming conventional techniques.

Keywords:
DNA microarray dataEnsemble classificationForest optimization algorithmGene selectionHybrid methodMulti-filterMulti-objective wrapper

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Machine Learning in Genomics

Background:

  • Microarray technology generates crucial DNA expression data for disease research.
  • Microarray datasets often suffer from small sample sizes, high dimensionality, and imbalanced classes, hindering classification model performance.
  • Efficient gene selection is vital for accurate analysis of complex biological data.

Purpose of the Study:

  • To develop a hybrid solution for effective gene selection and ensemble classifier construction for microarray data.
  • To address the challenges of dimensionality reduction and improve classification accuracy in gene expression analysis.
  • To introduce a novel multi-filter and adaptive chaotic multi-objective forest optimization algorithm (AC-MOFOA).

Main Methods:

  • A multi-filter preprocessing step combines five filter methods with a voting-based function to reduce gene dimensions.
  • An adaptive chaotic multi-objective forest optimization algorithm (AC-MOFOA) is employed as a wrapper method for simultaneous dimension reduction and KELM optimization.
  • An ensemble classifier is constructed using AC-MOFOA results for classifying microarray data.

Main Results:

  • The multi-filter approach effectively reduces gene subset size and selects relevant genes.
  • The proposed AC-MOFOA method improves KELM accuracy by reducing dataset dimensions, showing comparable or superior performance to other multi-objective methods.
  • The developed Ensemble Classifier achieves better classification accuracy and generalizability on most tested microarray datasets compared to conventional methods.

Conclusions:

  • The hybrid AC-MOFOA approach offers a robust solution for gene selection and classification of microarray data.
  • The proposed Ensemble Classifier demonstrates competitive performance against state-of-the-art methods, highlighting its potential for biological data analysis.
  • This study provides an effective strategy for enhancing the efficiency and accuracy of disease classification using high-dimensional gene expression data.