A robust meta-classification strategy for cancer diagnosis from gene expression data

Gabriela Alexe, Ramakrishna Ramaswamy, Gyan Bhanot, Jorge Lepre, Gustavo Stolovitzky, Babu Venkataraghavan, Arnold J. Levine

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

One of the major challenges in cancer diagnosis from microarray data is to develop robust classification models which are independent of the analysis techniques used and can combine data from different laboratories. We propose a metaclassification scheme which uses a robust multivariate gene selection procedure and integrates the results of several machine learning tools trained on raw and pattern data. We validate our method by applying it to distinguish diffuse large B-cell lymphoma (DLBCL) from follicular lymphoma (FL) on two independent datasets: the HuGeneFL Affmetrixy dataset of Shipp et al. (www.genome.wi.mit.du/MPR /lymphoma) and the Hu95Av2 Affymetrix dataset (DallaFavera's laboratory, Columbia University). Our meta-classification technique achieves higher predictive accuracies than each of the individual classifiers trained on the same dataset and is robust against various data perturbations. We also find that combinations of p53 responsive genes (e.g., p53, PLK1 and CDK2) are highly predictive of the phenotype.

Original languageEnglish (US)
Title of host publicationProceedings - 2005 IEEE Computational SystemsBioinformatics Conference, CSB 2005
Pages322-325
Number of pages4
DOIs
StatePublished - 2005
Externally publishedYes
Event2005 IEEE Computational Systems Bioinformatics Conference, CSB 2005 - Stanford, CA, United States
Duration: Aug 8 2005Aug 11 2005

Publication series

NameProceedings - 2005 IEEE Computational Systems Bioinformatics Conference, CSB 2005
Volume2005

Other

Other2005 IEEE Computational Systems Bioinformatics Conference, CSB 2005
Country/TerritoryUnited States
CityStanford, CA
Period8/8/058/11/05

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Medicine(all)

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