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CombiROC:一种交互式网络工具,用于选择组学数据的准确标记组合。

CombiROC: an interactive web tool for selecting accurate marker combinations of omics data.

机构信息

Istituto Nazionale Genetica Molecolare "Romeo ed Enrica Invernizzi", Milan, 20122, Italy.

DISSCO, Department of Clinical Sciences and Community Health, University of Milan, Milan,20122 Italy.

出版信息

Sci Rep. 2017 Mar 30;7:45477. doi: 10.1038/srep45477.

Abstract

Diagnostic accuracy can be improved considerably by combining multiple markers, whose performance in identifying diseased subjects is usually assessed via receiver operating characteristic (ROC) curves. The selection of multimarker signatures is a complicated process that requires integration of data signatures with sophisticated statistical methods. We developed a user-friendly tool, called CombiROC, to help researchers accurately determine optimal markers combinations from diverse omics methods. With CombiROC data from different domains, such as proteomics and transcriptomics, can be analyzed using sensitivity/specificity filters: the number of candidate marker panels rising from combinatorial analysis is easily optimized bypassing limitations imposed by the nature of different experimental approaches. Leaving to the user full control on initial selection stringency, CombiROC computes sensitivity and specificity for all markers combinations, performances of best combinations and ROC curves for automatic comparisons, all visualized in a graphic interface. CombiROC was designed without hard-coded thresholds, allowing a custom fit to each specific data: this dramatically reduces the computational burden and lowers the false negative rates given by fixed thresholds. The application was validated with published data, confirming the marker combination already originally described or even finding new ones. CombiROC is a novel tool for the scientific community freely available at http://CombiROC.eu.

摘要

通过结合多个标志物,可以显著提高诊断准确性,这些标志物在识别患病个体方面的性能通常通过接收者操作特征(ROC)曲线进行评估。多标志物特征的选择是一个复杂的过程,需要将数据特征与复杂的统计方法相结合。我们开发了一个用户友好的工具,称为 CombiROC,以帮助研究人员从各种组学方法中准确确定最佳标志物组合。使用 CombiROC,可以对来自不同领域的数据(如蛋白质组学和转录组学)进行分析,使用灵敏度/特异性过滤器:通过组合分析产生的候选标志物面板数量很容易通过不同实验方法的性质所施加的限制进行优化。CombiROC 允许用户完全控制初始选择的严格程度,为所有标志物组合计算灵敏度和特异性、最佳组合的性能以及自动比较的 ROC 曲线,所有这些都在图形界面中可视化。CombiROC 没有硬编码的阈值,允许针对每个特定数据集进行自定义拟合:这大大降低了计算负担,并降低了固定阈值给出的假阴性率。该应用程序已通过已发表的数据进行了验证,证实了已最初描述的标志物组合,甚至发现了新的标志物组合。CombiROC 是一个免费提供给科学界的新工具,可在 http://CombiROC.eu 上获得。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f823/5371980/820ad11339ad/srep45477-f1.jpg

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