期刊
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
卷 74, 期 7-8, 页码 491-503出版社
ELSEVIER IRELAND LTD
DOI: 10.1016/j.ijmedinf.2005.05.002
关键词
gene expression microarray analysis; decision support systems; neoplasms; diagnosis; computer-assisted artificial intelligence
资金
- NLM NIH HHS [R01 LM007948-01, P20 LM 007613-01] Funding Source: Medline
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据