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Related Concept Videos

Cluster Sampling Method01:20

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
Combinatorial Gene Control02:33

Combinatorial Gene Control

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Related Experiment Video

Updated: Jun 22, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Simultaneous genes and training samples selection by modified particle swarm optimization for gene expression data

Qi Shen1, Zhen Mei, Bao-Xian Ye

  • 1Department of Chemistry, Zhengzhou University, Zhengzhou, China. shenqi@zzu.edu.cn

Computers in Biology and Medicine
|June 2, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for selecting optimal genes and samples from gene expression data. The approach enhances patient classification accuracy and efficiently mines high-dimensional datasets.

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Last Updated: Jun 22, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Published on: March 1, 2024

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Machine Learning in Genomics

Background:

  • Gene expression datasets are crucial for patient diagnosis and classification.
  • Selecting informative genes and representative samples is vital for reducing data complexity.
  • Simultaneous identification and pruning of redundant genes and samples can improve classification performance.

Purpose of the Study:

  • To develop and evaluate a method for simultaneous optimal gene and sample selection.
  • To improve the efficiency and accuracy of patient classification using gene expression data.
  • To address the challenge of local optima in data reduction.

Main Methods:

  • Utilized a modified particle swarm optimization algorithm for simultaneous gene and sample selection.
  • Employed a support vector machine as the objective function to determine optimal gene and sample sets.
  • Validated the proposed method on three publicly available microarray datasets.

Main Results:

  • The proposed method effectively selects optimal genes and samples from high-dimensional gene expression data.
  • Demonstrated improved performance in classification tasks compared to existing methods.
  • Successfully applied to publicly available microarray datasets for evaluation.

Conclusions:

  • The developed method is a valuable tool for mining high-dimensional gene expression data.
  • Simultaneous gene and sample selection offers advantages in classification and data reduction.
  • The approach shows promise for enhancing diagnostic prediction in patient data.