Is the reduction of dimensionality to a small number of features always necessary in constructing predictive models for analysis of complex diseases or behaviours?
Is the reduction of dimensionality to a small number of features always necessary in constructing predictive models for analysis of complex diseases or behaviours?
复制标题
在构建用于分析复杂疾病或行为的预测模型时，是否始终需要将维度降低到少量特征？
DOI：
10.1109/iembs.2011.6090596
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发表时间：
2011
期刊：
影响因子：
--
通讯作者：
Alterovitz,Gil
中科院分区：
文献类型：
--
作者：
Zollanvari,Amin;Saccone,NancyL;Bierut,LauraJ;Ramoni,MarcoF;Alterovitz,Gil
基因表达和全基因组关联数据为研究人员提供了研究许多复杂性状和疾病的机会。当设计能够在该领域进行表型分类的预后和预测模型时，通过严格的过滤和/或特征选择来显著降低维度通常被认为是必要的。在这里，这项工作挑战这一假设，通过理论和实证分析。这项工作表明，通过适当的折衷所选模型的结构和特征的数量，即使在大的维度，一个是能够实现更好的性能。在分类规则中包含许多基因/变体可以帮助对复杂性状的分析提供新的思路，这些复杂性状通常由许多具有小效应大小的因果变体决定。
Gene expression and genome wide association data have provided researchers the opportunity to study many complex traits and diseases. When designing prognostic and predictive models capable of phenotypic classification in this area, significant reduction of dimensionality through stringent filtering and/or feature selection is often deemed imperative. Here, this work challenges this presumption through both theoretical and empirical analysis. This work demonstrates that by a proper compromise between structure of the selected model and the number of features, one is able to achieve better performance even in large dimensionality. The inclusion of many genes/variants in the classification rules can help shed new light on the analysis of complex traitstraits that are typically determined by many causal variants with small effect size.
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