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Subspace learning using low-rank latent representation learning and perturbation theorem: Unsupervised gene

Amir Moslemi1, Fariborz Baghaei Naeini2

  • 1Department of Physics, Toronto Metropolitan University, Ontario, Canada; School of Software Design & Data Science, Seneca Polytechnic, Toronto, ON, M4N 3M5, Canada; Physical Sciences, Sunnybrook Health Sciences Centre, Toronto, ON, M4N 3M5, Canada.

Computers in Biology and Medicine
|December 15, 2024
PubMed
Summary

This study introduces a novel unsupervised feature selection method using pseudo labels and perturbation theory for high-dimensional gene expression data. The approach enhances clustering accuracy and mutual information, outperforming existing methods.

Keywords:
Gene selectionLatent representation learningMicroarray datasetPerturbation theoryUnsupervised feature selection

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Area of Science:

  • Computational Biology
  • Machine Learning
  • Bioinformatics

Background:

  • Gene expression data analysis is crucial in machine learning and computational biology.
  • High-dimensional datasets (more features than samples) pose challenges like ill-posed systems and suboptimal algorithm performance.
  • Feature selection is preferred over feature extraction for its interpretability.

Purpose of the Study:

  • To propose a novel unsupervised feature selection method.
  • To address challenges in high-dimensional gene expression data analysis.
  • To improve the performance and interpretability of feature selection.

Main Methods:

  • Unsupervised feature selection based on pseudo label latent representation learning and perturbation theory.
  • Pseudo label extraction via latent representation learning.
  • Clustering features using k-means based on data matrix similarity and ranking with information gain.

Main Results:

  • The proposed method was evaluated on benchmark microarray and RNA-Sequencing datasets.
  • Numerical experiments demonstrated superior performance compared to eight state-of-the-art methods.
  • Key metrics for evaluation included clustering accuracy and normalized mutual information.

Conclusions:

  • The developed unsupervised feature selection technique effectively handles high-dimensional gene expression data.
  • The method shows significant improvements in clustering accuracy and normalized mutual information.
  • This approach offers a robust and interpretable solution for gene expression data analysis.