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Related Experiment Videos

Predicting cancer type with dimensionality-reduced gene expression micro-array data.

Marc Santoro1, Douglas A Talbert

  • 1Dept. of Computer Science, Tennessee Technological University, Cookeville, TN, USA.

AMIA ... Annual Symposium Proceedings. AMIA Symposium
|June 17, 2006
PubMed
Summary

High-dimensional gene expression data presents challenges for predictive modeling. Singular value decomposition effectively reduces this dimensionality, improving analysis of microarray data.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Gene expression data, particularly from microarrays, is characterized by high dimensionality.
  • Analyzing this data for predictive modeling is challenging due to the large number of genes relative to samples.

Purpose of the Study:

  • To investigate a dimensionality reduction technique for gene expression data.
  • To assess the utility of singular value decomposition (SVD) for handling high-dimensional microarray datasets.

Main Methods:

  • Singular value decomposition (SVD) was employed as a dimensionality reduction technique.
  • The method was applied to gene expression data derived from microarray experiments.

Main Results:

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  • Singular value decomposition effectively reduces the dimensionality of gene expression data.
  • This reduction facilitates more manageable and potentially more accurate predictive modeling.

    • Conclusions:

    • Dimensionality reduction using SVD is a viable strategy for analyzing high-dimensional gene expression data.
    • SVD offers a practical approach to overcome the challenges posed by microarray data complexity in predictive modeling.