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Related Concept Videos

Epigenetic Regulation01:37

Epigenetic Regulation

Epigenetic changes alter the physical structure of the DNA without changing the genetic sequence and often regulate whether genes are turned on or off. This regulation ensures that each cell produces only proteins necessary for its function. For example, proteins that promote bone growth are not produced in muscle cells. Epigenetic mechanisms play an essential role in healthy development. Conversely, precisely regulated epigenetic mechanisms are disrupted in diseases like cancer.
X-chromosome...
Epigenetic Regulation01:46

Epigenetic Regulation

Epigenetic mechanisms play an essential role in healthy development. Conversely, precisely regulated epigenetic mechanisms are disrupted in diseases like cancer.
Epigenetic Regulation01:46

Epigenetic Regulation

Epigenetic mechanisms play an essential role in healthy development. Conversely, precisely regulated epigenetic mechanisms are disrupted in diseases like cancer.
DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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: May 18, 2026

Pattern-based Search of Epigenomic Data Using GeNemo
06:38

Pattern-based Search of Epigenomic Data Using GeNemo

Published on: October 8, 2017

Exploring patterns of epigenetic information with data mining techniques.

Vanessa Aguiar-Pulido1, José A Seoane, Marcos Gestal

  • 1Department of Information and Communication Technologies, Computer Science Faculty, University of A Coruña, Campus de Elviña, S/N, 15071 A Coruña, Spain. vaguiar@udc.es

Current Pharmaceutical Design
|September 29, 2012
PubMed
Summary
This summary is machine-generated.

Data mining is crucial for analyzing vast epigenetic datasets to uncover heritable gene expression patterns. These patterns, alongside genetic mutations, are vital for understanding cancer development and aging.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Epigenetic data analysis has shifted towards high-throughput, genome-wide approaches, generating massive datasets.
  • Data mining, integrating statistics, AI, and database management, is essential for extracting meaningful patterns from this data.
  • Epigenetic information patterns influence gene expression, cellular differentiation, and fate, and can be heritable.

Purpose of the Study:

  • To review key applications of data mining techniques within the field of epigenetics.
  • To highlight the importance of data mining in analyzing complex epigenetic data.
  • To explore the role of data mining in identifying patterns related to cellular processes and disease.

Main Methods:

  • Review of existing literature on data mining applications in epigenetics.
  • Identification and categorization of data mining techniques used for epigenetic data analysis.
  • Synthesis of findings from various studies to illustrate practical applications.

Main Results:

  • Data mining facilitates the extraction of complex patterns from large-scale epigenetic datasets.
  • These patterns are crucial for understanding gene regulation, cellular differentiation, and heritability.
  • Applications include identifying epigenetic factors in aging and cancer development.

Conclusions:

  • Data mining is indispensable for advancing epigenetic research.
  • It enables the discovery of patterns linked to cellular fate and disease.
  • The integration of data mining with epigenetics holds significant potential for future discoveries.