Characterization of lung tumor subtypes through gene expression cluster validity assessment

Giorgio Valentini; Francesca Ruffino

RAIRO - Theoretical Informatics and Applications (2006)

  • Volume: 40, Issue: 2, page 163-176
  • ISSN: 0988-3754

Abstract

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The problem of assessing the reliability of clusters patients identified by clustering algorithms is crucial to estimate the significance of subclasses of diseases detectable at bio-molecular level, and more in general to support bio-medical discovery of patterns in gene expression data. In this paper we present an experimental analysis of the reliability of clusters discovered in lung tumor patients using DNA microarray data. In particular we investigate if subclasses of lung adenocarcinoma can be detected with high reliability at bio-molecular level. To this end we apply cluster validity measures based on random projections recently proposed by Bertoni and coworkers. The results show that at least two subclasses of lung adenocarcinoma can be detected with relatively high reliability, confirming and extending previous findings reported in the literature.

How to cite

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Valentini, Giorgio, and Ruffino, Francesca. "Characterization of lung tumor subtypes through gene expression cluster validity assessment." RAIRO - Theoretical Informatics and Applications 40.2 (2006): 163-176. <http://eudml.org/doc/249627>.

@article{Valentini2006,
abstract = { The problem of assessing the reliability of clusters patients identified by clustering algorithms is crucial to estimate the significance of subclasses of diseases detectable at bio-molecular level, and more in general to support bio-medical discovery of patterns in gene expression data. In this paper we present an experimental analysis of the reliability of clusters discovered in lung tumor patients using DNA microarray data. In particular we investigate if subclasses of lung adenocarcinoma can be detected with high reliability at bio-molecular level. To this end we apply cluster validity measures based on random projections recently proposed by Bertoni and coworkers. The results show that at least two subclasses of lung adenocarcinoma can be detected with relatively high reliability, confirming and extending previous findings reported in the literature. },
author = {Valentini, Giorgio, Ruffino, Francesca},
journal = {RAIRO - Theoretical Informatics and Applications},
keywords = {Cluster validity; clustering algorithms; bio-molecular taxonomy of tumors; DNA microarray data analysis.; DNA microarray data analysis},
language = {eng},
month = {7},
number = {2},
pages = {163-176},
publisher = {EDP Sciences},
title = {Characterization of lung tumor subtypes through gene expression cluster validity assessment},
url = {http://eudml.org/doc/249627},
volume = {40},
year = {2006},
}

TY - JOUR
AU - Valentini, Giorgio
AU - Ruffino, Francesca
TI - Characterization of lung tumor subtypes through gene expression cluster validity assessment
JO - RAIRO - Theoretical Informatics and Applications
DA - 2006/7//
PB - EDP Sciences
VL - 40
IS - 2
SP - 163
EP - 176
AB - The problem of assessing the reliability of clusters patients identified by clustering algorithms is crucial to estimate the significance of subclasses of diseases detectable at bio-molecular level, and more in general to support bio-medical discovery of patterns in gene expression data. In this paper we present an experimental analysis of the reliability of clusters discovered in lung tumor patients using DNA microarray data. In particular we investigate if subclasses of lung adenocarcinoma can be detected with high reliability at bio-molecular level. To this end we apply cluster validity measures based on random projections recently proposed by Bertoni and coworkers. The results show that at least two subclasses of lung adenocarcinoma can be detected with relatively high reliability, confirming and extending previous findings reported in the literature.
LA - eng
KW - Cluster validity; clustering algorithms; bio-molecular taxonomy of tumors; DNA microarray data analysis.; DNA microarray data analysis
UR - http://eudml.org/doc/249627
ER -

References

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