GEMS: A system for automated cancer diagnosis and biomarker discovery from microarray gene expression data
GEMS: A system for automated cancer diagnosis and biomarker discovery from microarray gene expression data
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DOI：
10.1016/j.ijmedinf.2005.05.002
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发表时间：
2005-08-01
影响因子：
4.9
通讯作者：
Aliferis, CF
中科院分区：
文献类型：
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作者：
Statnikov, A;Tsamardinos, I;Aliferis, CF
癌症患者治疗的成功取决于准确的诊断。为此，我们构建了一个名为 GEMS（基因表达模型选择器）的系统，用于自动开发和评估高质量的癌症诊断模型以及从微阵列基因表达数据中发现生物标志物。为了确定并为系统配备该领域性能最佳的诊断方法，我们首先使用 11 个癌症微阵列数据集对分类算法进行了全面评估。在本文中，我们使用五个新数据集对系统进行了初步评估。 GEMS 自动生成的模型的性能与人类分析师获得的结果相当或更好。此外，我们对系统进行了跨数据集评估。这涉及使用数据集构建诊断模型并估计其未来性能，然后应用该模型并在不同数据集上评估其性能。我们发现 GEMS 生成的模型确实在独立样本中表现良好，此外，交叉验证性能估计系统的输出非常接近独立验证获得的误差。 GEMS 可从 http://www.gems-system.org 免费下载用于非商业用途。 (C) 2005 Elsevier Ireland Ltd. 保留所有权利。
The success of treatment of patients with cancer depends on establishing an accurate diagnosis. To this end, we have built a system called GEMS (gene expression model selector) for the automated development and evaluation of high-quality cancer diagnostic models and biomarker discovery from microarray gene expression data. In order to determine and equip the system with the best performing diagnostic methodologies in this domain, we first conducted a comprehensive evaluation of classification algorithms using 11 cancer microarray datasets. In this paper we present a preliminary evaluation of the system with five new datasets. The performance of the models produced automatically by GEMS is comparable or better than the results obtained by human analysts. Additionally, we performed a cross-dataset evaluation of the system. This involved using a dataset to build a diagnostic model and to estimate its future performance, then applying this model and evaluating its performance on a different dataset. We found that models produced by GEMS indeed perform well in independent samples and, furthermore, the cross-validation performance estimates output by the system approximate well the error obtained by the independent validation. GEMS is freely available for download for non-commercial use from http://www.gems-system.org. (C) 2005 Elsevier Ireland Ltd. All rights reserved.