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Several body functions deteriorate with age. The external signs of aging are easily identifiable. For example, the skin becomes dry, less elastic, and thins out, forming wrinkles. The skin of the face begins to appear looser due to a decrease in the levels of elastic and collagen fibers in the connective tissue. Additionally, melanin production in the hair follicle decreases with age, resulting in gray hair. Moreover, the senses of sight and hearing decline, so glasses and hearing aids may...
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Updated: Jun 6, 2026

A Quantitative Fitness Analysis Workflow
11:39

A Quantitative Fitness Analysis Workflow

Published on: August 13, 2012

Computational biology for ageing.

Daniela Wieser1, Irene Papatheodorou, Matthias Ziehm

  • 1EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.

Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences
|December 1, 2010
PubMed
Summary
This summary is machine-generated.

Researchers can access and analyze public genomic and proteomic data to understand ageing. This review details available bioinformatics tools and methods for interpreting ageing research, highlighting future challenges and insights.

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Last Updated: Jun 6, 2026

A Quantitative Fitness Analysis Workflow
11:39

A Quantitative Fitness Analysis Workflow

Published on: August 13, 2012

Area of Science:

  • Gerontology and Bioinformatics
  • Genomics and Proteomics
  • Computational Biology

Background:

  • High-throughput technologies generate vast amounts of public data on ageing.
  • Computational analysis of ageing data is crucial for understanding its causes and effects.
  • Accessibility of data and tools accelerates ageing research.

Purpose of the Study:

  • To describe available databases and computational tools for ageing research.
  • To outline computational approaches for interpreting ageing data.
  • To review biological insights from bioinformatics analyses in ageing.

Main Methods:

  • Database and tool curation for ageing research.
  • Description of computational approaches: gene expression, comparative, and pathway analyses.
  • Review of bioinformatics studies on ageing across different organisms and conditions.

Main Results:

  • Identification of key databases and computational tools relevant to ageing.
  • Summary of established and emerging bioinformatics methodologies for ageing data interpretation.
  • Compilation of recent biological discoveries derived from ageing data analysis.

Conclusions:

  • Bioinformatics is essential for advancing the understanding of ageing.
  • Availability of data and tools facilitates interdisciplinary ageing research.
  • Future developments in computational methods will further elucidate ageing mechanisms.