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2005
IEEE

A Robust Meta-classification Strategy for Cancer Diagnosis from Gene Expression Data

10 years 7 months ago
A Robust Meta-classification Strategy for Cancer Diagnosis from Gene Expression Data
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 metaclassification 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)...
Gabriela Alexe, Gyan Bhanot, Babu Venkataraghavan,
Added 24 Jun 2010
Updated 24 Jun 2010
Type Conference
Year 2005
Where CSB
Authors Gabriela Alexe, Gyan Bhanot, Babu Venkataraghavan, Ramakrishna Ramaswamy, Jorge Lepre, Arnold J. Levine, Gustavo Stolovitzky
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