Maulik Ujjwal, Chakraborty Debasis
IEEE Trans Nanobioscience. 2014 Jun;13(2):152-60. doi: 10.1109/TNB.2014.2312132.
DNA microarray data now permit scientists to screen thousand of genes simultaneously and determine whether those genes are active or silent in normal and cancerous tissues. With the advancement of microarray technology, new analytical methods must be developed to find out whether microarray data have discriminative signatures of gene expression over normal or cancerous tissues. In this paper, we attempt a prediction scheme that combines fuzzy preference based rough set (FPRS) method for feature (gene) selection with semisupervised SVMs. To show the effectiveness of the proposed approach, we compare the performance of this technique with the signal-to-noise ratio (SNR) and consistency based feature selection (CBFS) methods. Using six benchmark gene microarray datasets (including both binary and multi-class classification problems), we demonstrate experimentally that our proposed scheme can achieve significant empirical success and is biologically relevant for cancer diagnosis and drug discovery.
DNA微阵列数据现在使科学家能够同时筛选数千个基因，并确定这些基因在正常组织和癌组织中是活跃还是沉默。随着微阵列技术的进步，必须开发新的分析方法来找出微阵列数据是否具有区分正常组织或癌组织的基因表达特征。在本文中，我们尝试了一种预测方案，该方案将基于模糊偏好的粗糙集（FPRS）方法用于特征（基因）选择，并与半监督支持向量机相结合。为了展示所提方法的有效性，我们将该技术的性能与信噪比（SNR）和基于一致性的特征选择（CBFS）方法进行了比较。使用六个基准基因微阵列数据集（包括二元和多类分类问题），我们通过实验证明，我们提出的方案可以取得显著的经验性成功，并且在癌症诊断和药物发现方面具有生物学相关性。
