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Data mining for case-based reasoning in high-dimensional biological domains

机译:高维生物学领域中基于案例的推理的数据挖掘

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Case-based reasoning (CBR) is a suitable paradigm for class discovery in molecular biology, where the rules that define the domain knowledge are difficult to obtain and the number and the complexity of the rules affecting the problem are too large for formal knowledge representation. To extend the capabilities of CBR, we propose the mixture of experts for case-based reasoning (MOE4CBR), a method that combines an ensemble of CBR classifiers with spectral clustering and logistic regression. Our approach not only achieves higher prediction accuracy, but also leads to the selection of a subset of features that have meaningful relationships with their class labels. We evaluate MOE4CBR by applying the method to a CBR system called TA3 - a computational framework for CBR systems. For two ovarian mass spectrometry data sets, the prediction accuracy improves from 80 percent to 93 percent and from 90 percent to 98.4 percent, respectively. We also apply the method to leukemia and lung microarray data sets with prediction accuracy improving from 65 percent to 74 percent and from 60 percent to 70 percent, respectively. Finally, we compare our list of discovered biomarkers with the lists of selected biomarkers from other studies for the mass spectrometry data sets.
机译:基于案例的推理(CBR)是分子生物学中类别发现的合适范例,其中很难获得定义领域知识的规则,影响问题的规则的数量和复杂性对于形式化知识表示而言太大。为了扩展CBR的功能,我们提出了基于案例的推理(MOE4CBR)的专家组合,该方法将CBR分类器与频谱聚类和逻辑回归相结合。我们的方法不仅实现了更高的预测准确性,而且还导致选择了与其类标签具有有意义关系的要素子集。我们通过将该方法应用于称为TA3的CBR系统(CBR系统的计算框架)来评估MOE4CBR。对于两个卵巢质谱数据集,预测准确性分别从80%提高到93%,从90%提高到98.4%。我们还将这种方法应用于白血病和肺微阵列数据集,其预测准确性分别从65%提高到74%和从60%提高到70%。最后,我们将发现的生物标记物列表与其他研究的质谱数据集的所选生物标记物列表进行比较。

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