InSyBio bioonets:基于网络的生物标志物发现的有效工具

K. Theofilatos, Christos M. Dimitrakopoulos, Christos E. Alexakos, A. Korfiati, S. Likothanassis, S. Mavroudi
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引用次数: 4

摘要

生物网络已广泛应用于系统生物学,以模拟分子参与者,如蛋白质,基因,mrna,非编码rna等的复杂相互作用。然而,目前大多数生物标志物的发现方法都没有使用生物网络,而只是使用简单的统计方法来识别差异表达基因和基因产物。在本文中,我们介绍了InSyBio BioNets,这是一个基于云的网络平台,提供独特的生物标志物发现管道,它结合了差异表达分析和比较生物网络的方法来有效地识别生物标志物。作为案例研究,InSyBio BioNets应用于帕金森基因表达测量数据集,通过恢复更紧凑和信息丰富的生物标志物集,优于标准统计方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
InSyBio BioNets: an efficient tool for network-based biomarker discovery
Biological networks have been widely used in systems biology in order to model the complex interactions of molecular players such as proteins, genes, mRNAs, non-coding RNAs and others. However, most of the current methods for biomarker discovery do not use biological networks, but just deploy simple statistical methods to identify differentially expressed genes and gene products. In the present paper, we present InSyBio BioNets, which is a cloud-based web platform offering a unique biomarker discovery pipeline, which combines differential expression analysis and a method for comparing biological networks to identify biomarkers efficiently. As a case study, InSyBio BioNets was applied to a Parkinson dataset of gene expression measurements and outperformed a standard statistical approach by recovering a more compact and informative set of biomarkers.
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