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Methods, computer-accessible medium and systems to model disease progression using biomedical data from multiple patients

机译:使用来自多名患者的生物医学数据模拟疾病进展的方法,计算机可访问的培养基和系统

摘要

An exemplary embodiment of system, method and computer-accessible medium can be provided to reconstruct models based on the probabilistic notion of causation, which can differ fundamentally from that can be based on correlation. A general reconstruction setting can be complicated by the presence of noise in the data, owing to the intrinsic variability of biological processes as well as experimental or measurement errors. To gain immunity to noise in the reconstruction performance, it is possible to use a shrinkage estimator. On synthetic data, the exemplary procedure can outperform currently known procedures and, for some real cancer datasets, there are biologically significant differences revealed by the exemplary reconstructed progressions. The exemplary system, method and computer accessible medium can be efficient even with a relatively low number of samples and its performance quickly converges to its asymptote as the number of samples increases.
机译:可以提供系统,方法和计算机可访问介质的示例性实施例以基于原因的概率概念来重建模型,这可以基本上与之基于相关性。 由于生物过程的内在变化以及实验或测量误差,通过数据存在噪声,一般重建设置可以复杂。 为了在重建性能中获得噪声的免疫力,可以使用收缩估计器。 在合成数据上,示例性过程可以优于当前已知的程序,并且对于一些真实的癌症数据集,通过示例性重建进展揭示了生物学上显着的差异。 即使在相对较少的样本数量增加时,示例性系统,方法和计算机可访问介质也可以高效地具有相对较少的样本,并且随着样本的数量增加,其性能将其性能很快收敛于其渐近。

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