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ALIASING IN GENE FEATURE DETECTION BY PROJECTIVE METHODS

机译：通过投射方法进行基因特征检测的别名

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Because of measurements obtained under limited experimental conditions or time points compared to the presence of many genes, also known as the "large dimension, small sample size" problem, dimensionality reduction techniques are a common practice in statistical bioinformatics involving microarray analysis. However, in order to improve the performance of reverse engineering and statistical inference procedures aimed to estimate gene-gene connectivity links, some kind of regularization is usually needed to reduce the overall data complexities, together with ad hoc feature selection to uncover biologically relevant gene associations. The paper deals with feature selection by projective methods; in particular, it addresses some issues: Can the impact of noise on the data be limited by shrinkage or de-noising? How can complexity from convoluted dynamics associated with microarray measurements be discounted? In modeling such data, how to deal with over-parametrization, and control it? The problem of aliasing is then discussed and classified into two categories according to the trade-off between biological relevance and noise, and finally reported in analytical form via subspace analysis.
机译：由于相比于许多基因的存在，在有限的实验条件或时间点获得的测量值（也称为“大尺寸，小样本量”问题），降维技术是涉及微阵列分析的统计生物信息学中的一种常见实践。但是，为了提高旨在估计基因-基因连通性链接的逆向工程和统计推断程序的性能，通常需要进行某种形式的规范化处理以降低总体数据复杂性，并通过特设特征选择来发现生物学上相关的基因关联。本文通过投影方法进行特征选择。它特别解决了一些问题：噪声对数据的影响是否可以通过缩小或降噪来限制？如何降低与微阵列测量相关的复杂的动力学复杂性？在对此类数据进行建模时，如何处理过度参数化并加以控制？然后讨论混叠问题，并根据生物学相关性和噪声之间的权衡取舍，将其分为两类，最后通过子空间分析以分析形式进行报告。

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