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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.

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Related Experiment Video

Updated: May 19, 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

Interaction-based feature selection and classification for high-dimensional biological data.

Haitian Wang1, Shaw-Hwa Lo, Tian Zheng

  • 1Department of ISOM, HKUST, Clear Water Bay, Kowloon, Hong Kong.

Bioinformatics (Oxford, England)
|September 5, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for gene-gene interaction analysis in complex diseases. Incorporating interactions improves disease prediction accuracy and biological insight discovery.

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Last Updated: May 19, 2026

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

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Published on: October 11, 2018

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
03:37

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Published on: March 1, 2024

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Epistasis (gene-gene interaction) is crucial for complex diseases but challenging to detect due to combinatorial complexity.
  • Understanding gene-gene interactions is vital for deciphering the genetic architecture of common human diseases.

Purpose of the Study:

  • To develop a novel feature selection method that incorporates variable interactions for improved analysis of high-dimensional biological data.
  • To enhance the accuracy of disease prediction and gain deeper biological insights by accounting for gene-gene interactions.

Main Methods:

  • A new feature selection approach integrating variable interactions was developed.
  • The method was applied to three gene expression datasets, evaluating variable quality via classification error rates and biological relevance.
  • The approach is applicable to various high-dimensional data types beyond gene expression.

Main Results:

  • Considering gene-gene interactions significantly reduced classification error rates.
  • The method identified genes relevant to breast cancer metastasis, with notable overlap with the Gene-to-System Breast Cancer (G2SBC) database.
  • Selected genes demonstrated potential biological implications for disease mechanisms.

Conclusions:

  • Interaction-based methods offer substantial improvements in biological insight discovery.
  • The proposed method leads to more accurate predictions for complex diseases by accounting for gene-gene interactions.
  • This approach advances the field of genetic analysis for complex diseases and high-dimensional data.