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Related Concept Videos

Frequency-dependent Selection01:21

Frequency-dependent Selection

When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.Positive Frequency-Dependent SelectionIn positive...
Combinatorial Gene Control02:33

Combinatorial Gene Control

Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
The expression of more than 30,000 genes is controlled by approximately 2000-3000 transcription factors. This is possible because a single transcription factor can recognize more than one regulatory sequence. The specificity in gene...
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This number is...
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.
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Genetic Screens02:46

Genetic Screens

Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which result in visible changes...

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Related Experiment Video

Updated: Jun 11, 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

A greedy algorithm for gene selection based on SVM and correlation.

Mingjun Song1, Sanguthevar Rajasekaran

  • 1Department of Computer Science and Engineering, University of Connecticut, Storrs 06269, CT, USA. mjsong@engr.uconn.edu

International Journal of Bioinformatics Research and Applications
|July 10, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces GCI-SVM, a new method for gene selection in microarray analysis. It improves cancer classification accuracy with fewer genes compared to existing techniques.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Microarrays enable high-throughput gene expression analysis in biological samples.
  • Gene expression data is crucial for understanding cellular processes and disease states.
  • Identifying key genes (gene selection) is vital for accurate disease classification.

Purpose of the Study:

  • To develop a novel algorithm for informative gene selection from microarray data.
  • To enhance classification accuracy in cancer detection using minimal gene sets.
  • To improve upon existing gene selection methods in terms of efficiency and performance.

Main Methods:

  • The proposed GCI-SVM algorithm integrates Support Vector Machines (SVMs) with gene correlation analysis.
  • This approach aims to identify a minimal yet informative set of genes.
  • The method is evaluated against established algorithms in the field.

Main Results:

  • The GCI-SVM algorithm achieved higher classification accuracy compared to existing methods.
  • It successfully identified a smaller subset of genes crucial for classification.
  • Experimental results demonstrate the effectiveness of the combined SVM and gene correlation approach.

Conclusions:

  • GCI-SVM offers a more efficient and accurate approach to gene selection in microarray data analysis.
  • The method holds promise for improved diagnostic tools in cancer research.
  • Combining machine learning with correlation analysis is a powerful strategy for gene selection.