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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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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Published on: October 11, 2018

f-Information measures for efficient selection of discriminative genes from microarray data.

Pradipta Maji1

  • 1Machine Intelligence Unit, Indian Statistical Institute, Kolkata 700 108, India. pmaji@isical.ac.in

IEEE Transactions on Bio-Medical Engineering
|March 11, 2009
PubMed
Summary

New f-information measures effectively select relevant genes from microarray data for diagnostics. These methods outperform mutual information, achieving 100% accuracy in cancer datasets.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Microarray gene expression data contains numerous genes, but only a subset is crucial for diagnostics.
  • Mutual information is a common method for selecting relevant and non-redundant genes.
  • Information theory offers alternative measures, such as f-information measures, for gene selection.

Purpose of the Study:

  • To evaluate various f-information measures as criteria for gene selection from microarray data.
  • To compare the performance of f-information measures against mutual information for diagnostic applications.

Main Methods:

  • Utilized f-information measures to compute gene-gene redundancy and gene-class relevance by assessing distribution divergence.
  • Compared the predictive accuracy of classifiers (Naive Bayes, K-NN, SVM) using gene sets selected by f-information measures and mutual information.
  • Tested the methods on breast cancer, leukemia, and colon cancer microarray datasets.

Main Results:

  • Certain f-information measures demonstrated effectiveness in selecting relevant and non-redundant genes.
  • Some f-information measures achieved 100% prediction accuracy across all three cancer datasets.
  • Mutual information achieved 100% accuracy only for breast cancer, with lower accuracies for leukemia (98.6%) and colon cancer (93.6%).

Conclusions:

  • F-information measures represent a promising approach for gene selection in microarray data analysis.
  • These measures offer superior performance compared to mutual information for cancer diagnostics.
  • The study highlights the potential of f-information measures for improving diagnostic accuracy in genomics.