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Related Concept Videos

Protein Folding01:22

Protein Folding

Overview
Protein Folding01:22

Protein Folding

Overview
Protein Folding01:25

Protein Folding

Proteins are chains of amino acids linked together by peptide bonds. Upon synthesis, a protein folds into a three-dimensional conformation, critical to its biological function. Interactions between its constituent amino acids guide protein folding, and hence the protein structure is primarily dependent on its amino acid sequence.
Protein Structure Is Critical to Its Biological Function
Proteins perform a wide range of biological functions such as catalyzing chemical reactions, providing...
Evolutionary Relationships through Genome Comparisons02:54

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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...
Protein Families02:47

Protein Families

Protein families are groups of homologous proteins; that is, they have similarities in amino acid sequences and three-dimensional structures. Protein families usually occur because of gene duplication, where an additional copy of a gene is inserted into the genome of an organism.   Mutations that change the amino acids but still allow the protein to be properly synthesized, will lead to new protein family members.   If these new proteins contain similar amino acids in key locations, protein...
Protein Families02:47

Protein Families

Protein families are groups of homologous proteins; that is, they have similarities in amino acid sequences and three-dimensional structures. Protein families usually occur because of gene duplication, where an additional copy of a gene is inserted into the genome of an organism.   Mutations that change the amino acids but still allow the protein to be properly synthesized, will lead to new protein family members.   If these new proteins contain similar amino acids in key locations, protein...

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Related Experiment Video

Updated: Jun 20, 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

Protein fold classification with genetic algorithms and feature selection.

Peng Chen1, Chunmei Liu, Legand Burge

  • 1Department of Systems and Computer Science, Howard University, 2300 Sixth Street, NW, Washington, DC 20059, USA. pchen78@scs.howard.edu

Journal of Bioinformatics and Computational Biology
|September 29, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method using genetic algorithms and feature selection for protein fold classification, achieving 71.28% accuracy. This approach enhances protein tertiary structure prediction by identifying optimal feature subsets.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Structural Biology

Background:

  • Protein fold classification is crucial for predicting protein tertiary structures.
  • Accurate classification aids in understanding protein function and evolution.
  • Existing methods face challenges in efficiently selecting relevant features.

Purpose of the Study:

  • To develop a novel, accurate, and efficient approach for protein fold classification.
  • To leverage genetic algorithms (GAs) and feature selection for improved classification rates.
  • To enhance the prediction of protein tertiary structures.

Main Methods:

  • A hybrid approach combining genetic algorithms and support vector machines (SVMs).
  • Genetic algorithms iteratively search for optimal feature selection functions.
  • SVMs evaluate the fitness of each individual (feature subset) based on classification accuracy.

Main Results:

  • The proposed method achieved a 71.28% accuracy on a benchmark dataset of 27 protein folds.
  • This accuracy surpasses current state-of-the-art protein fold prediction methods.
  • The approach effectively identifies discriminative features for fold classification.

Conclusions:

  • The integration of genetic algorithms and feature selection offers a powerful strategy for protein fold classification.
  • This method provides a significant advancement in predicting protein tertiary structures.
  • The approach demonstrates high performance and potential for broader applications in structural bioinformatics.