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An ensemble method for gene discovery based on DNA microarray data

Academic Article
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Overview

authors

  • Li, X.
  • Rao, S. Q.
  • Zhang, T. W.
  • Guo, Z.
  • Zhang, Qinghai
  • Moser, K. L.
  • Topol, Eric

publication date

  • October 2004

journal

  • Science in China Series C-Life Sciences  Journal

abstract

  • The advent of DNA microarray technology has offered the promise of casting new insights onto deciphering secrets of life by monitoring activities of thousands of genes simultaneously. Current analyses of microarray data focus on precise classification of biological types, for example, tumor versus normal tissues. A further scientific challenging task is to extract disease-relevant genes from the bewildering amounts of raw data, which is one of the most critical themes in the post-genomic era, but it is generally ignored due to lack of an efficient approach. In this paper, we present a novel ensemble method for gene extraction that can be tailored to fulfill multiple biological tasks including (i) precise classification of biological types; (ii) disease gene mining; and (iii) target-driven gene networking. We also give a numerical application for (i) and (ii) using a public microarrary data set and set aside a separate paper to address (iii).

subject areas

  • Algorithms
  • Computational Biology
  • Databases as Topic
  • Genomics
  • Markov Chains
  • Models, Genetic
  • Models, Statistical
  • Models, Theoretical
  • Oligonucleotide Array Sequence Analysis
  • Software
  • Statistics as Topic
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Research

keywords

  • ensemble decision
  • feature gene selection
  • microarrays
  • recursive partition tree
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Identity

International Standard Serial Number (ISSN)

  • 1006-9305

Digital Object Identifier (DOI)

  • 10.1360/03yc0127

PubMed ID

  • 15623151
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Additional Document Info

start page

  • 396

end page

  • 405

volume

  • 47

issue

  • 5

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