Alexe Gabriela, Bhanot Gyan, Venkataraghavan Babu, Ramaswamy Ramakrishna, Lepre Jorge, Levine Arnold J, Stolovitzky Gustavo
IBM Computational Biology Center, IBM T.J. Watson Research Center, Yorktown Heights, NY 10598, USA.
Proc IEEE Comput Syst Bioinform Conf. 2005:322-5. doi: 10.1109/csb.2005.7.
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 meta-classification 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.
从微阵列数据进行癌症诊断的主要挑战之一是开发强大的分类模型，该模型独立于所使用的分析技术，并且能够整合来自不同实验室的数据。我们提出了一种元分类方案，该方案使用强大的多变量基因选择程序，并整合了在原始数据和模式数据上训练的几种机器学习工具的结果。我们通过将其应用于两个独立数据集来验证我们的方法，以区分弥漫性大B细胞淋巴瘤（DLBCL）和滤泡性淋巴瘤（FL）：Shipp等人的HuGeneFL Affmetrixy数据集（www.genome.wi.mit.du/MPR/lymphoma）以及Hu95Av2 Affymetrix数据集（哥伦比亚大学DallaFavera实验室）。我们的元分类技术比在同一数据集上训练的每个单独分类器都具有更高的预测准确性，并且对各种数据干扰具有鲁棒性。我们还发现p53反应基因（例如p53、PLK1和CDK2）的组合对表型具有高度预测性。
