非参数差异网络分析:揭示特定分子特征的工具。

IF 2.9 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS
Pietro Hiram Guzzi, Arkaprava Roy, Marianna Milano, Pierangelo Veltri
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引用次数: 0

摘要

背景:在各种条件下,分子相互作用的重构会导致不同的表型结果。差异网络分析(DINA)致力于探索基因和蛋白质网络中的这些重配。利用统计学习和图论,差分网络分析算法可以仔细检查实验数据中相互作用模式的变化:我们引入了一种新颖的差异网络分析方法,将基于性和性别属性的差异基因表达纳入其中。我们假设,基因表达可以通过非高斯过程得到准确表达。我们的方法包括量化基因对之间非参数相关性的变化以及单个基因的表达水平:结论:将我们的方法应用于有关糖尿病和肝组织动脉粥样硬化的公开表达数据集,我们发现了性别差异网络。结果强调了我们的方法在发现有意义的分子差异方面的生物学相关性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Non parametric differential network analysis: a tool for unveiling specific molecular signatures.

Background: The rewiring of molecular interactions in various conditions leads to distinct phenotypic outcomes. Differential network analysis (DINA) is dedicated to exploring these rewirings within gene and protein networks. Leveraging statistical learning and graph theory, DINA algorithms scrutinize alterations in interaction patterns derived from experimental data.

Results: Introducing a novel approach to differential network analysis, we incorporate differential gene expression based on sex and gender attributes. We hypothesize that gene expression can be accurately represented through non-Gaussian processes. Our methodology involves quantifying changes in non-parametric correlations among gene pairs and expression levels of individual genes.

Conclusions: Applying our method to public expression datasets concerning diabetes mellitus and atherosclerosis in liver tissue, we identify gender-specific differential networks. Results underscore the biological relevance of our approach in uncovering meaningful molecular distinctions.

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来源期刊
BMC Bioinformatics
BMC Bioinformatics 生物-生化研究方法
CiteScore
5.70
自引率
3.30%
发文量
506
审稿时长
4.3 months
期刊介绍: BMC Bioinformatics is an open access, peer-reviewed journal that considers articles on all aspects of the development, testing and novel application of computational and statistical methods for the modeling and analysis of all kinds of biological data, as well as other areas of computational biology. BMC Bioinformatics is part of the BMC series which publishes subject-specific journals focused on the needs of individual research communities across all areas of biology and medicine. We offer an efficient, fair and friendly peer review service, and are committed to publishing all sound science, provided that there is some advance in knowledge presented by the work.
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