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Feature Selection and Classification of MAQC-II Breast Cancer and Multiple Myeloma Microarray Gene Expression Data

机译：MAQC-II乳腺癌和多发性骨髓瘤微阵列基因表达数据的特征选择和分类

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摘要

Microarray data has a high dimension of variables but available datasets usually have only a small number of samples, thereby making the study of such datasets interesting and challenging. In the task of analyzing microarray data for the purpose of, e.g., predicting gene-disease association, feature selection is very important because it provides a way to handle the high dimensionality by exploiting information redundancy induced by associations among genetic markers. Judicious feature selection in microarray data analysis can result in significant reduction of cost while maintaining or improving the classification or prediction accuracy of learning machines that are employed to sort out the datasets. In this paper, we propose a gene selection method called Recursive Feature Addition (RFA), which combines supervised learning and statistical similarity measures. We compare our method with the following gene selection methods: class="unordered" style="list-style-type:disc">Support Vector Machine Recursive Feature Elimination (SVMRFE)Leave-One-Out Calculation Sequential Forward Selection (LOOCSFS)Gradient based Leave-one-out Gene Selection (GLGS) To evaluate the performance of these gene selection methods, we employ several popular learning classifiers on the MicroArray Quality Control phase II on predictive modeling (MAQC-II) breast cancer dataset and the MAQC-II multiple myeloma dataset. Experimental results show that gene selection is strictly paired with learning classifier. Overall, our approach outperforms other compared methods. The biological functional analysis based on the MAQC-II breast cancer dataset convinced us to apply our method for phenotype prediction. Additionally, learning classifiers also play important roles in the classification of microarray data and our experimental results indicate that the Nearest Mean Scale Classifier (NMSC) is a good choice due to its prediction reliability and its stability across the three performance measurements: Testing accuracy, MCC values, and AUC errors.
机译：微阵列数据具有高维度的变量，但是可用的数据集通常只有少量样本，因此使得对此类数据集的研究变得有趣而具有挑战性。在出于例如预测基因-疾病关联的目的而分析微阵列数据的任务中，特征选择非常重要，因为特征选择通过利用遗传标记之间的关联引起的信息冗余提供了处理高维的方法。微阵列数据分析中明智的特征选择可以显着降低成本，同时保持或改善用于整理数据集的学习机的分类或预测精度。在本文中，我们提出了一种称为递归特征加法（RFA）的基因选择方法，该方法结合了监督学习和统计相似性度量。我们将我们的方法与以下基因选择方法进行了比较： class =“ unordered” style =“ list-style-type：disc”> <！-list-behavior = unordered prefix-word = mark-type = disc max- label-size = 0-> 支持向量机递归特征消除（SVMRFE） 留一法计算顺序正向选择（LOOCSFS） 基于梯度的Leave-一次性基因选择（GLGS） 为了评估这些基因选择方法的性能，我们在预测建模（MAQC-II）乳腺癌数据集的MicroArray质量控制II期上采用了几种流行的学习分类器和MAQC-II多发性骨髓瘤数据集。实验结果表明，基因选择与学习分类器严格配对。总体而言，我们的方法优于其他比较方法。基于MAQC-II乳腺癌数据集的生物学功能分析使我们相信可以将我们的方法用于表型预测。此外，学习分类器在微阵列数据分类中也起着重要作用，我们的实验结果表明，最近平均尺度分类器（NMSC）是一个不错的选择，因为其预测可靠性和在三种性能测量中的稳定性：测试准确性，MCC值和AUC错误。

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