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Related Concept Videos

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...
RNA-seq03:21

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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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...
What is Gene Expression?01:36

What is Gene Expression?

A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is comprised  of nucleotides and proteins are comprised of amino acids, a mediator is required to convert the information encoded in DNA into proteins. This mediator is the messenger RNA (mRNA). mRNA copies the blueprint from DNA by a process called transcription. In eukaryotes, transcription occurs in the nucleus by complementary base-pairing with the DNA template. The mRNA is then processed and...
What is Gene Expression?01:42

What is Gene Expression?

Overview
Gene expression is the process in which DNA directs the synthesis of functional products, that is, proteins. Cells can regulate gene expression at various stages. It allows organisms to generate different cell types and enables cells to adapt to internal and external factors.
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A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is made up of nucleotides and proteins consist of amino...
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Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
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Comparing algorithms for clustering of expression data: how to assess gene clusters.

Golan Yona1, William Dirks, Shafquat Rahman

  • 1Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY, USA.

Methods in Molecular Biology (Clifton, N.J.)
|April 22, 2009
PubMed
Summary

Evaluating gene expression data clustering is crucial. This study introduces a novel method combining internal and external validity indices to select optimal clustering algorithms for gene networks.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Clustering gene expression data is vital for identifying co-regulated genes.
  • Numerous clustering algorithms exist, yielding diverse results that require objective evaluation.
  • Assessing the quality of clustering solutions is challenging without robust validation methods.

Purpose of the Study:

  • To present a novel method for assessing gene expression data clustering algorithms.
  • To combine internal and external validity indices for superior model selection.
  • To compare different clustering algorithms based on biological relevance and accuracy.

Main Methods:

  • Developed a hybrid validation approach using Minimum Description Length (MDL) principle for internal validity.
  • Incorporated an external validity index measuring consistency with experimental data and functional gene links.
  • Tested the method on various clustering algorithms, including those designed for noisy data.

Main Results:

  • The combined internal and external validity indices effectively pinpoint the optimal clustering model.
  • The proposed method successfully identifies clustering algorithms that maximize correlation with gene networks.
  • Demonstrated the ability to minimize error rates and assess cluster significance in relation to biochemical pathways.

Conclusions:

  • A robust method for evaluating gene expression clustering algorithms has been established.
  • The combined validity index approach offers a superior strategy for selecting optimal clustering models.
  • This work facilitates more accurate identification of gene clusters and their biological significance.