从基因表达谱数据中挖掘预测乳腺癌组织学分级的信息。
Mining microarray data to predict the histological grade of a breast cancer.
机构信息
LIRMM UM2 CNRS, UMR 5506 - CC 477, 161 rue Ada, 34095 Montpellier Cedex 5, France.
LIRMM UM2 CNRS, UMR 5506 - CC 477, 161 rue Ada, 34095 Montpellier Cedex 5, France; MIAp UM3, Université Paul-Valery, Route de Mende, 34199 Montpellier Cedex, France.
出版信息
J Biomed Inform. 2011 Dec;44 Suppl 1:S12-S16. doi: 10.1016/j.jbi.2011.03.002. Epub 2011 Mar 21.
BACKGROUND
The aim of this study was to develop an original method to extract sets of relevant molecular biomarkers (gene sequences) that can be used for class prediction and can be included as prognostic and predictive tools.
MATERIALS AND METHODS
The method is based on sequential patterns used as features for class prediction. We applied it to classify breast cancer tumors according to their histological grade.
RESULTS
We obtained very good recall and precision for grades 1 and 3 tumors, but, like other authors, our results were less satisfactory for grade 2 tumors.
CONCLUSIONS
We demonstrated the interest of sequential patterns for class prediction of microarrays and we now have the material to use them for prognostic and predictive applications.
背景
本研究旨在开发一种原始方法来提取可用于分类预测的相关分子生物标志物（基因序列）集，并可作为预后和预测工具。
材料和方法
该方法基于用作分类预测特征的序列模式。我们将其应用于根据组织学分级对乳腺癌肿瘤进行分类。
结果
我们获得了 1 级和 3 级肿瘤非常好的召回率和精度，但与其他作者一样，我们的结果对 2 级肿瘤的效果不太理想。
结论
我们证明了序列模式在微阵列分类预测中的重要性，并且我们现在有了用于预后和预测应用的材料。
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