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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...
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

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Published on: October 11, 2018

Finding consistent disease subnetworks across microarray datasets.

Donny Soh1, Difeng Dong, Yike Guo

  • 1National University of Singapore, 13 Computing Drive, Singapore 117417. donnysoh@gmail.com

BMC Bioinformatics
|March 1, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces SNet, a novel technique for analyzing microarray data to improve reproducibility. SNet identifies significant "subnetworks" within biological pathways, offering more consistent and biologically relevant results than existing methods.

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Area of Science:

  • Bioinformatics
  • Genomics
  • Systems Biology

Background:

  • Microarray analysis often yields inconsistent results across different datasets for the same disease.
  • Reproducibility is a significant challenge in analyzing diverse microarray data.

Purpose of the Study:

  • To introduce SNet, a new technique for quantitative and descriptive analysis of microarray data.
  • To address the reproducibility issue in microarray analysis by identifying significant pathway subnetworks.

Main Methods:

  • Developed SNet to identify connected, significant portions of biological pathways, termed 'subnetworks'.
  • Applied SNet to independent microarray datasets for childhood ALL, DMD, and lung cancer.
  • Compared SNet's performance against Gene Set Enrichment Analysis (GSEA), t-test, and Significance Analysis of Microarrays (SAM).

Main Results:

  • SNet consistently identified similar significant subnetworks across independent datasets for each disease.
  • Gene-level agreement for SNet subnetworks ranged from 51.18% to 93.01%.
  • Existing methods showed lower agreement (GSEA: 2.38%-28.90%, t-test: 49.60%-73.01%, SAM: 49.96%-81.25%) and produced smaller, less substantial subnetworks.

Conclusions:

  • SNet generates more consistent and reproducible subnetworks and genes compared to GSEA, t-test, and SAM.
  • The larger subnetworks identified by SNet are likely more biologically significant and less spurious.
  • Validated subnetworks with literature confirm SNet's ability to derive descriptive biological conclusions.