Dutkowski Janusz, Gambin Anna
Institute of Informatics, Warsaw University, Banacha 2 02-097 Warsaw, Poland.
BMC Bioinformatics. 2007 May 24;8 Suppl 5(Suppl 5):S5. doi: 10.1186/1471-2105-8-S5-S5.
Recent development of mass spectrometry technology enabled the analysis of complex peptide mixtures. A lot of effort is currently devoted to the identification of biomarkers in human body fluids like serum or plasma, based on which new diagnostic tests for different diseases could be constructed. Various biomarker selection procedures have been exploited in recent studies. It has been noted that they often lead to different biomarker lists and as a consequence, the patient classification may also vary.
Here we propose a new approach to the biomarker selection problem: to apply several competing feature ranking procedures and compute a consensus list of features based on their outcomes. We validate our methods on two proteomic datasets for the diagnosis of ovarian and prostate cancer.
The proposed methodology can improve the classification results and at the same time provide a unified biomarker list for further biological examinations and interpretation.
质谱技术的最新发展使得对复杂肽混合物的分析成为可能。目前,人们致力于在血清或血浆等人体体液中识别生物标志物,在此基础上可以构建针对不同疾病的新型诊断测试。近年来的研究采用了各种生物标志物选择程序。人们注意到,这些程序往往会导致不同的生物标志物列表,因此患者分类也可能有所不同。
在此,我们提出了一种解决生物标志物选择问题的新方法:应用几种相互竞争的特征排序程序,并根据其结果计算特征的共识列表。我们在两个用于诊断卵巢癌和前列腺癌的蛋白质组学数据集上验证了我们的方法。
所提出的方法可以改善分类结果,同时为进一步的生物学检查和解释提供统一的生物标志物列表。