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A novel classification technique for cancer diagnostics based on microarray gene expression profiling (MGEP)

机译：基于微阵列基因表达分析（MGEP）的癌症诊断的一种新型分类技术

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Pattern Recognition (PR) plays a vital role in the field of Bioinformatics. Various techniques of PR are used to analyze, segment and manipulate the high dimensional microarray gene expression data for classification. Microarray Gene Expression Profiling (MGEP) is an important domain of Bioinformatics that yields such high dimensional data used for various clinical applications such as cancer diagnostics and drug designing. In this study a novel scheme has been developed for the classification of unknown malignant tumors into known classes. The classification scheme includes the transformation of high dimensional microarray data on to the Mahalanobis space before classification, in order to compensate for the data spreads corresponding to each class and every gene. The efficiency of the proposed classification scheme has been proven on 10 publicly available cancer datasets containing both binary and multiclass data. To improve the performance of the classifier gene selection technique is applied on the datasets as a preprocessing/data extraction step.
机译：模式识别（PR）在生物信息学领域起着至关重要的作用。 PR的各种技术用于分析，分段和操纵对分类的高尺寸微阵列基因表达数据。微阵列基因表达分析（MGEP）是生物信息学的重要结构域，其产生用于各种临床应用的这种高尺寸数据，例如癌症诊断和药物设计。在本研究中，已经开发了一种新的方案，用于将未知的恶性肿瘤分类为已知课程。分类方案包括在分类之前将高维微阵列数据转换为mahalanobis空间，以便补偿对应于每个类和每个基因的数据扩展。已在包含二进制和多字符数据的10个公共癌症数据集上证明了所提出的分类方案的效率。为了改善分类器基因选择技术的性能作为预处理/数据提取步骤将在数据集上应用。

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