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2005
2.	Alexe, Gabriela; Ramaswamy, Ramakrishna; Bhanot, Gyan; Lepre, Jorge; Stolovitzky, Gustavo; Venkataraghavan, Babu; Levine, Arnold J A robust meta-classification strategy for cancer diagnosis from gene expression data Journal Article Proceedings – 2005 IEEE Computational Systems Bioinformatics Conference, CSB 2005, 2005 , pp. 322–325, 2005, ISBN: 0769523447. Abstract \| Links \| BibTeX \| Tags: Bioinformatics, cancer diagnosis @article{Alexe2005, title = {A robust meta-classification strategy for cancer diagnosis from gene expression data}, author = {Gabriela Alexe and Ramakrishna Ramaswamy and Gyan Bhanot and Jorge Lepre and Gustavo Stolovitzky and Babu Venkataraghavan and Arnold J Levine}, url = {https://ramramaswamy.org/papers/R37.pdf}, doi = {10.1109/CSB.2005.7}, isbn = {0769523447}, year = {2005}, date = {2005-01-01}, journal = {Proceedings – 2005 IEEE Computational Systems Bioinformatics Conference, CSB 2005}, volume = {2005}, pages = {322–325}, 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. textcopyright 2005 IEEE.}, keywords = {Bioinformatics, cancer diagnosis}, pubstate = {published}, tppubtype = {article} } Close 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. textcopyright 2005 IEEE. Close https://ramramaswamy.org/papers/R37.pdf doi:10.1109/CSB.2005.7 Close
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1.	Alexe, Gabriela Bhanot Gyan; Vengataraghavan, Babu; Lepre, Jorge; Levine, Arnold J; Stolovitzky, Gustavo Robust meta-analysis of genomic data for cancer diagnosis Journal Article pp. 7–8, 0000. Links \| BibTeX \| Tags: cancer diagnosis, combinatorial biomarkers, meta-analysis, meta-classifiers, patterns @article{Bhanot, title = {Robust meta-analysis of genomic data for cancer diagnosis}, author = {Gabriela Bhanot Gyan Alexe and Babu Vengataraghavan and Jorge Lepre and Arnold J Levine and Gustavo Stolovitzky}, url = {https://ramramaswamy.org/papers/R36.pdf}, pages = {7–8}, keywords = {cancer diagnosis, combinatorial biomarkers, meta-analysis, meta-classifiers, patterns}, pubstate = {published}, tppubtype = {article} } Close https://ramramaswamy.org/papers/R36.pdf Close

2005

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