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Related Concept Videos

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...
Gene Conversion02:08

Gene Conversion

Other than maintaining genome stability via DNA repair, homologous recombination plays an important role in diversifying the genome. In fact, the recombination of sequences forms the molecular basis of genomic evolution. Random and non-random permutations of genomic sequences create a library of new amalgamated sequences. These newly formed genomes can determine the fitness and survival of cells. In bacteria, homologous and non-homologous types of recombination lead to the evolution of new...
Gene Conversion02:08

Gene Conversion

Other than maintaining genome stability via DNA repair, homologous recombination plays an important role in diversifying the genome. In fact, the recombination of sequences forms the molecular basis of genomic evolution. Random and non-random permutations of genomic sequences create a library of new amalgamated sequences. These newly formed genomes can determine the fitness and survival of cells. In bacteria, homologous and non-homologous types of recombination lead to the evolution of new...
Gene Families01:57

Gene Families

Gene families consist of groups of genes proposed to have originated from a common ancestor. Typically these arise through events in which a gene or genes are mistakenly duplicated during cell division. Unlike their parent genes (which are subject to selection pressure to maintain function), these gene copies do not need to preserve their sequences and may evolve at a relatively faster rate.
Occasionally these regions can be adapted to take on new roles within the organism, becoming novel genes...
Gene Families01:57

Gene Families

Gene families consist of groups of genes proposed to have originated from a common ancestor. Typically these arise through events in which a gene or genes are mistakenly duplicated during cell division. Unlike their parent genes (which are subject to selection pressure to maintain function), these gene copies do not need to preserve their sequences and may evolve at a relatively faster rate.
Occasionally these regions can be adapted to take on new roles within the organism, becoming novel genes...

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Related Experiment Video

Updated: Jun 26, 2026

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information
09:37

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information

Published on: August 15, 2019

High-performance gene name normalization with GeNo.

Joachim Wermter1, Katrin Tomanek, Udo Hahn

  • 1Jena University Language and Information Engineering Lab, Friedrich-Schiller-Universität Jena, Fürstengraben 30, 07743 Jena, Germany. joachim.wermter@uni-jena.de

Bioinformatics (Oxford, England)
|February 4, 2009
PubMed
Summary
This summary is machine-generated.

GeNo is a new system for gene name normalization that achieves high accuracy. It uses symbolic and statistical methods, offering a reproducible and competitive solution for identifying gene and protein names in biomedical text.

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A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes
09:10

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

Published on: May 22, 2018

Related Experiment Videos

Last Updated: Jun 26, 2026

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information
09:37

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information

Published on: August 15, 2019

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes
09:10

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

Published on: May 22, 2018

Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Natural Language Processing

Background:

  • Accurate recognition and normalization of gene and protein names are crucial in biomedicine.
  • Gene name ambiguity across species and with common words presents significant challenges.
  • Existing methods struggle with the complexity of gene name identification.

Purpose of the Study:

  • To develop a highly competitive system for gene name normalization.
  • To address the challenges of gene name ambiguity and improve recognition accuracy.
  • To ensure the reproducibility of gene normalization results.

Main Methods:

  • Developed GeNo, a system combining symbolic and statistical approaches.
  • Utilized publicly available software and data resources.
  • Incorporated extensive background knowledge through semantic profiling.

Main Results:

  • Achieved an F-measure of 86.4% (precision: 87.8%, recall: 85.0%) on the BioCreAtIvE-II test set.
  • Demonstrated performance on par with the best existing systems for gene name normalization.
  • Presented a lucid architecture for full reproducibility.

Conclusions:

  • GeNo offers a robust and accurate solution for gene name normalization.
  • The system's reliance on public resources and clear architecture promotes wider adoption.
  • GeNo is available for use and deployment in biomedical search engines.