Rodríguez Jesús A, Rivero Luis, Sánchez-Peña Matilde L, Isaza Clara E, Cabrera-Ríos Mauricio
Department of Industrial Engineering, University of Puerto Rico at Mayagüez.
P R Health Sci J. 2010 Sep;29(3):305-11.
Diagnosing cancer using microarray analysis to study differential gene expression has been a recent focus of intense research Although several very sophisticated analysis tools have been developed with this aim in mind, it still remains a challenge to keep these methods free of parametric adjustments as well as maintain their transparency for the final user. Nonparametric methods in general have been associated with these last two characteristics, thus becoming attractive tools for microarray analysis in cancer research. In particular, diagnosing cancer via microarray analysis is an exercise whereby tissue is characterized according to its differential gene expression levels. In this manuscript, two novel nonparametric methods for cancer diagnosis using microarray data are described and their performance assessed against a baseline approach that utilizes the Mann-Whitney test for median differences. Both methods show promising results in terms of their potential use in making diagnoses.
利用微阵列分析研究差异基因表达来诊断癌症是近期研究的一个热点。尽管已经开发了几种非常复杂的分析工具来实现这一目标，但要使这些方法无需参数调整并保持对最终用户的透明度，仍然是一项挑战。一般来说，非参数方法具有这两个特点，因此成为癌症研究中微阵列分析的有吸引力的工具。特别是，通过微阵列分析诊断癌症是一种根据组织的差异基因表达水平对其进行表征的做法。在本手稿中，描述了两种使用微阵列数据进行癌症诊断的新型非参数方法，并将它们的性能与使用曼-惠特尼检验来检验中位数差异的基线方法进行了比较。这两种方法在用于诊断方面都显示出了有前景的结果。
