检测特征排序的影响点。

IF 2.6 4区生物学 Q2 BIOLOGY

Computational Biology and Chemistry Pub Date : 2025-01-04 DOI:10.1016/j.compbiolchem.2024.108339

Shuo Wang , Junyan Lu

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引用次数: 0

摘要

背景：特征排序在生物信息学中是至关重要的，但往往被忽视的影响点（IPs）所扭曲。本研究旨在探讨ip对特征排名的影响，并提出ip检测方法。方法：我们使用left -one-out方法，通过比较删除后的排名变化来评估每个案例对特征排名的影响。通过一种新的排名比较方法来衡量排名变化，该方法使用自适应的最高优先级权重，该权重可根据排名变化的分布进行调整。我们的IP检测方法在几个公共数据集上进行了评估。结果：我们的方法在几个TCGA基因表达数据集中识别出潜在的ip，表明ip可以严重扭曲特征排名。这些排名的变化最终会影响后续的分析，如富集通路，这表明在得出特征排名时，有必要进行ip检测。结论：ip显著影响功能排名和后续分析；常规IP检测是必要的，但未得到充分利用。我们的方法在R包findIPs中可用。

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Detect influential points of feature rankings

Background

Feature rankings are crucial in bioinformatics but can be distorted by influential points (IPs), which are often overlooked. This study aims to investigate the impact of IPs on feature rankings and propose IPs detection method

Method

We use a leave-one-out approach to assess each case's influence on feature rankings by comparing rank changes after its removal. The rank changes are measured by a novel rank comparison method that involves using adaptive top-prioritized weights that are adjustable to the distribution of rank changes. Our IP detection method was evaluated on several public datasets.

Results

Our method identified potential IPs in several TCGA gene expression datasets, revealing that IPs can severely distort feature rankings. These rank changes can ultimately affect subsequent analyses such as enriched pathways, suggesting the necessity of IPs detection when deriving feature rankings.

Conclusions

IPs significantly impact feature rankings and subsequent analyses; routine IP detection is necessary yet underutilized. Our method is available in the R package findIPs.

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来源期刊

Computational Biology and Chemistry 生物-计算机：跨学科应用

CiteScore

6.10

自引率

3.20%

发文量

142

审稿时长

24 days

期刊介绍： Computational Biology and Chemistry publishes original research papers and review articles in all areas of computational life sciences. High quality research contributions with a major computational component in the areas of nucleic acid and protein sequence research, molecular evolution, molecular genetics (functional genomics and proteomics), theory and practice of either biology-specific or chemical-biology-specific modeling, and structural biology of nucleic acids and proteins are particularly welcome. Exceptionally high quality research work in bioinformatics, systems biology, ecology, computational pharmacology, metabolism, biomedical engineering, epidemiology, and statistical genetics will also be considered. Given their inherent uncertainty, protein modeling and molecular docking studies should be thoroughly validated. In the absence of experimental results for validation, the use of molecular dynamics simulations along with detailed free energy calculations, for example, should be used as complementary techniques to support the major conclusions. Submissions of premature modeling exercises without additional biological insights will not be considered. Review articles will generally be commissioned by the editors and should not be submitted to the journal without explicit invitation. However prospective authors are welcome to send a brief (one to three pages) synopsis, which will be evaluated by the editors.