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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...

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Related Experiment Video

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Perturbations of Circulating miRNAs in Irritable Bowel Syndrome Detected Using a Multiplexed High-throughput Gene Expression Platform
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Perturbation and candidate analysis to combat overfitting of gene expression microarray data.

Ravi Mathur1, J David Schaffer, Walker H Land

  • 1Department of Bioengineering, Binghamton University, Binghamton, NY 13902, USA.

International Journal of Computational Biology and Drug Design
|December 27, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method using a genetic algorithm-support vector machine (GA-SVM) hybrid to reduce over-fitting in gene expression data. Initial findings suggest a binary search approach effectively identifies key biological signals in cancer datasets.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Gene expression microarray datasets are prone to over-fitting due to high dimensionality (many features) and limited samples (few cases).
  • Over-fitting leads to spurious patterns, hindering the identification of genuine biological signals.

Purpose of the Study:

  • To develop a robust method to combat over-fitting in gene expression data analysis.
  • To identify methods for determining the strongest biological signal within complex datasets.

Main Methods:

  • A hybrid approach combining a genetic algorithm (GA) with a support vector machine (SVM) was employed.
  • Gaussian noise was introduced to aid in discovering minimal feature sets.
  • Cross-validation was used to assess classification accuracy.
  • A binary search strategy was applied to identify the most significant signals.

Main Results:

  • The GA-SVM hybrid successfully reduced over-fitting and identified minimal feature sets for accurate classification.
  • Initial results on a colorectal cancer dataset demonstrated the effectiveness of the binary search approach.
  • The method identified a modest number of candidate genes representing the strongest biological signal.

Conclusions:

  • The proposed GA-SVM hybrid method offers a promising solution for analyzing high-dimensional gene expression data.
  • Binary search is an effective strategy for pinpointing significant biological signals in cancer genomics.
  • This approach aids in reliable biomarker discovery from microarray data.