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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%...

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Related Experiment Video

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

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Comparison of feature selection methods for cross-laboratory microarray analysis.

Hsi-Che Liu1, Pei-Chen Peng, Tzung-Chien Hsieh

  • 1Mackay Medical College and Division of Pediatric Hematology-Oncology, Mackay Memorial Hospital, New Taipei.

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|October 5, 2013
PubMed
Summary
This summary is machine-generated.

Significance Analysis of Microarrays (SAM) and Random Forest (RF) show strong performance for cross-laboratory gene expression analysis. SAM offers the best classification accuracy in Affymetrix microarray studies.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Gene expression data from microarrays is rapidly increasing.
  • Integrated analysis of cross-laboratory data is crucial for extensive studies.
  • Selecting appropriate feature selection methods is essential for accurate analysis.

Purpose of the Study:

  • To compare four feature selection methods for Affymetrix microarray data across different laboratories.
  • To evaluate the performance of (t)-test, Significance Analysis of Microarrays (SAM), Rank Products (RP), and Random Forest (RF).
  • To analyze the impact of training data size, number of selected genes, and data imbalance.

Main Methods:

  • Applied four feature selection methods: (t)-test, SAM, RP, and RF.
  • Utilized rank-based normalization to mitigate cross-laboratory bias.
  • Evaluated methods on Affymetrix data for leukemia, breast, and lung cancers using balanced accuracy.

Main Results:

  • SAM demonstrated the best classification performance.
  • RF achieved high accuracy but was less stable than SAM.
  • (t)-test performed the worst among the evaluated methods.

Conclusions:

  • SAM is the most effective feature selection method for cross-laboratory Affymetrix microarray analysis.
  • RF is a viable alternative, though stability is a consideration.
  • The study highlights the importance of method selection for robust cross-lab genomic data integration.