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Related Concept Videos

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...
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

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Updated: Jun 1, 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

[An overview of feature selection algorithm in bioinformatics].

Xin Li1, Li Ma, Jinjia Wang

  • 1Institute of Biomedical Engineering, Yanshan University, Qinhuangdao 066004, China. yddylixin@ysu.edu.cn

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi = Journal of Biomedical Engineering = Shengwu Yixue Gongchengxue Zazhi
|May 25, 2011
PubMed
Summary
This summary is machine-generated.

Feature selection (FS) techniques reduce high-dimensional bioinformatics data to its core components. This aids in analyzing complex biological data, offering insights into sequence, microarray, and mass spectra analyses.

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Published on: October 11, 2018

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Context:

  • High-dimensional biological datasets are common in bioinformatics.
  • Small sample sizes often accompany high-dimensional data.
  • Analyzing complex biological data requires efficient dimensionality reduction.

Purpose:

  • To introduce feature selection (FS) techniques in bioinformatics.
  • To explain the fundamental principles of FS algorithms.
  • To explore the applications of FS in various biological analyses.

Summary:

  • Feature selection (FS) is crucial for identifying significant, low-dimensional data from high-dimensional biological datasets.
  • FS algorithms are essential for uncovering underlying patterns in complex biological data, particularly in fields like sequence analysis, microarray analysis, and mass spectrometry.
  • This review covers FS techniques, their properties, applications, and future directions in bioinformatics.

Impact:

  • Enables more effective analysis of complex biological data.
  • Facilitates the discovery of hidden patterns and rules within datasets.
  • Highlights the importance and future potential of FS in advancing bioinformatics research.