基于集成学习的组学数据关联差分表达模式研究。

IF 0.9 4区 数学 Q3 Mathematics
Jorge M Arevalillo, Raquel Martin-Arevalillo
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引用次数: 0

摘要

高通量技术的不断发展使得同时监测数百或数千个生物输入的表达水平成为可能,同时也增加了被称为基因组数据源的内容。在分析这些数据源时,一个相关问题涉及到在两种实验条件、临床状态或两类生物学结果之间检测差异表达。虽然已经开发了大量的单变量数据分析方法来解决这个问题,但评估差异表达的相互作用模式的策略在文献中很少,并且仅限于临时解决方案。本文通过利用像随机森林这样的集成学习算法的设施来解决这个问题,提出了一种评估由组学变量相互作用解释的差异表达的度量,因此可能会发现微妙的生物模式。袋外错误率是随机森林分类器预测精度的估计,它被用作副产品,提出了一种评估差异表达交互模式的新措施。它的性能在合成场景中进行了研究,也应用于对SARS-CoV-2和结肠癌数据的实际研究,在这些研究中,它揭示了其他方法未发现的关联。我们的建议旨在提供一种新的方法,可以帮助生物医学和生命科学专家揭示有见地的相互作用模式,从而可能破译生物学和临床结果背后的分子机制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Patterns of differential expression by association in omic data using a new measure based on ensemble learning.

The ongoing development of high-throughput technologies is allowing the simultaneous monitoring of the expression levels for hundreds or thousands of biological inputs with the proliferation of what has been coined as omic data sources. One relevant issue when analyzing such data sources is concerned with the detection of differential expression across two experimental conditions, clinical status or two classes of a biological outcome. While a great deal of univariate data analysis approaches have been developed to address the issue, strategies for assessing interaction patterns of differential expression are scarce in the literature and have been limited to ad hoc solutions. This paper contributes to the problem by exploiting the facilities of an ensemble learning algorithm like random forests to propose a measure that assesses the differential expression explained by the interaction of the omic variables so subtle biological patterns may be uncovered as a result. The out of bag error rate, which is an estimate of the predictive accuracy of a random forests classifier, is used as a by-product to propose a new measure that assesses interaction patterns of differential expression. Its performance is studied in synthetic scenarios and it is also applied to real studies on SARS-CoV-2 and colon cancer data where it uncovers associations that remain undetected by other methods. Our proposal is aimed at providing a novel approach that may help the experts in biomedical and life sciences to unravel insightful interaction patterns that may decipher the molecular mechanisms underlying biological and clinical outcomes.

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来源期刊
CiteScore
1.20
自引率
11.10%
发文量
8
审稿时长
6-12 weeks
期刊介绍: Statistical Applications in Genetics and Molecular Biology seeks to publish significant research on the application of statistical ideas to problems arising from computational biology. The focus of the papers should be on the relevant statistical issues but should contain a succinct description of the relevant biological problem being considered. The range of topics is wide and will include topics such as linkage mapping, association studies, gene finding and sequence alignment, protein structure prediction, design and analysis of microarray data, molecular evolution and phylogenetic trees, DNA topology, and data base search strategies. Both original research and review articles will be warmly received.
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