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Machine Learning Based Computational Gene Selection Models: A Survey, Performance Evaluation, Open Issues, and Future

Nivedhitha Mahendran1, P M Durai Raj Vincent1, Kathiravan Srinivasan1

  • 1School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India.

Frontiers in Genetics
|December 28, 2020
PubMed
Summary

Machine learning techniques effectively reduce high-dimensional genomic data, addressing overfitting in gene expression analysis. This review categorizes methods for improved tumor diagnosis and highlights challenges in small sample datasets.

Keywords:
gene selectionmachine learningmicroarray gene expressionsupervised gene selectionunsupervised gene selection

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Gene Expression, vital for physical traits, involves DNA to RNA information flow and protein synthesis.
  • Microarray DNA technology captures gene expression but produces high-dimensional, small-sample data, leading to model overfitting.
  • Machine learning (ML) is increasingly used in genomics to address complex data challenges.

Purpose of the Study:

  • To conduct an extensive review of recent Machine Learning-based gene selection approaches for dimensionality reduction in gene expression data.
  • To analyze the performance of various ML methods, particularly in the context of tumor diagnosis.
  • To categorize gene selection algorithms and discuss open issues in handling high-dimensional, low-sample-size genomic data.

Main Methods:

  • Review of literature on Machine Learning-based gene selection algorithms.
  • Categorization of algorithms into Supervised, Unsupervised, and Semi-supervised learning.
  • Performance analysis of selected methods for tumor diagnosis applications.

Main Results:

  • Machine learning offers effective strategies for gene selection and dimensionality reduction in gene expression data.
  • Supervised, Unsupervised, and Semi-supervised learning approaches show promise in improving data handling.
  • Analysis indicates the utility of these methods in enhancing tumor diagnostic accuracy.

Conclusions:

  • Machine learning-based gene selection is crucial for overcoming the challenges of high-dimensional, low-sample-size genomic data.
  • Further research is needed to address existing open issues in data analysis and model development.
  • The reviewed methods provide a foundation for advancing genomic studies and clinical applications like tumor diagnosis.