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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...
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
Ribosome Profiling02:24

Ribosome Profiling

Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique helps...
Bioequivalence Data: Statistical Interpretation01:16

Bioequivalence Data: Statistical Interpretation

The statistical interpretation of bioequivalence data is a significant aspect of pharmaceutical research. Bioequivalence refers to the absence of any significant difference in the rate and extent to which the active ingredient in pharmaceutical products becomes available at the site of drug action when administered at the same molar dose under similar conditions. This helps determine if different drug products have similar absorption rates, ensuring their interchangeability.Statistical...
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...
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.
Genetic Information Flows from DNA to RNA to Protein
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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Related Experiment Video

Updated: Jun 18, 2026

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
10:10

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

Measuring similarity between gene expression profiles: a Bayesian approach.

Viet-Anh Nguyen1, Pietro Lió

  • 1Computer Laboratory, University of Cambridge, Cambridge, CB3 0FD, UK. van25@cam.ac.uk

BMC Genomics
|December 5, 2009
PubMed
Summary
This summary is machine-generated.

A new metric, BayesGen, improves gene similarity analysis for microarray data. This method enhances gene co-expression network construction and cancer tissue clustering, leading to better biological insights.

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Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards
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Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

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Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
10:10

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards
10:50

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

Published on: February 25, 2017

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Gene expression profile similarity is crucial for predicting functional modules.
  • Traditional metrics like Euclidean distance and Pearson correlation have limitations with high-throughput microarray data.

Purpose of the Study:

  • To introduce BayesGen, a novel metric for measuring gene expression profile similarity.
  • To evaluate BayesGen's performance in constructing genome-wide co-expression networks and clustering cancer tissues.

Main Methods:

  • BayesGen is formulated as an evidence ratio between gene-pair generating mechanism hypotheses.
  • It incorporates global dataset characteristics as prior knowledge.
  • Joint modeling of intensity levels and noise variances addresses nonlinearity and noise associations.

Main Results:

  • BayesGen demonstrates effective similarity extraction from microarray data.
  • Applications include improved genome-wide co-expression network construction.
  • Successful clustering of human cancer tissues into subtypes was achieved.

Conclusions:

  • BayesGen offers a more effective approach to extracting gene similarity information from microarray data.
  • This leads to significant improvements in various inference tasks.
  • The metric shows robustness for other object-feature data types.