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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

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Published on: August 30, 2013

Molecular pattern discovery based on penalized matrix decomposition.

Chun-Hou Zheng1, Lei Zhang, Vincent To-Yee Ng

  • 1College of Electrical Engineering and Automation, Anhui University, Hefei, Anhui 230039, China. zhengch99@126.com

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|April 27, 2011
PubMed
Summary
This summary is machine-generated.

Penalized matrix decomposition (PMD) effectively clusters tumor gene expression data, identifying complex cancer types and determining the optimal number of clusters for improved biological phenotype discovery.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Accurate tumor identification is vital for effective cancer treatment.
  • Gene expression data analysis, particularly tumor clustering, is a powerful tool for cancer class discovery.

Purpose of the Study:

  • To apply penalized matrix decomposition (PMD) for extracting metasamples from gene expression data for enhanced tumor clustering.
  • To evaluate PMD's ability to identify complex cancer classes and determine cluster numbers compared to conventional methods.

Main Methods:

  • Utilized penalized matrix decomposition (PMD) on gene expression data to extract representative metasamples.
  • Employed PMD factors as class indicators for clustering and determining the optimal number of clusters.

Main Results:

  • The PMD-based method successfully extracted inherent structures within samples of the same class.
  • PMD demonstrated superior performance in identifying samples with complex classes compared to hierarchical clustering (HC), self-organizing maps (SOM), affinity propagation (AP), and nonnegative matrix factorization (NMF).
  • PMD factors served as a reliable index for determining the appropriate number of clusters and explained inconsistencies in classifications from conventional methods.

Conclusions:

  • The proposed PMD-based method offers a promising approach for discovering biological phenotypes from gene expression data.
  • PMD facilitates the identification of complex tumor subtypes and provides a robust method for cluster number determination.
  • This method enhances the understanding of gene expression data, including the discovery of gene modules in conterminous developmental stages.