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使用KeyPathwayMiner 5进行强大的从头途径富集分析。

Robust de novo pathway enrichment with KeyPathwayMiner 5.

作者信息

Alcaraz Nicolas, List Markus, Dissing-Hansen Martin, Rehmsmeier Marc, Tan Qihua, Mollenhauer Jan, Ditzel Henrik J, Baumbach Jan

机构信息

Department of Mathematics and Computer Science, University of Southern Denmark, 5230 Odense, Denmark; Department of Cancer and Inflammation Research, Institute of Molecular Medicine, University of Southern Denmark, 5000 Odense, Denmark.

Department of Mathematics and Computer Science, University of Southern Denmark, 5230 Odense, Denmark; Department of Cancer and Inflammation Research, Institute of Molecular Medicine, University of Southern Denmark, 5000 Odense, Denmark; Lundbeckfonden Center of Excellence in Nanomedicine NanoCAN, University of Southern Denmark, 5000 Odense, Denmark; Institute of Clinical Research, University of Southern Denmark, 5000 Odense, Denmark; Max Planck Institute for Informatics, 66123 Saarbrucken, Germany.

出版信息

F1000Res. 2016 Jun 28;5:1531. doi: 10.12688/f1000research.9054.1. eCollection 2016.

Abstract

Identifying functional modules or novel active pathways, recently termed de novo pathway enrichment, is a computational systems biology challenge that has gained much attention during the last decade. Given a large biological interaction network, KeyPathwayMiner extracts connected subnetworks that are enriched for differentially active entities from a series of molecular profiles encoded as binary indicator matrices. Since interaction networks constantly evolve, an important question is how robust the extracted results are when the network is modified. We enable users to study this effect through several network perturbation techniques and over a range of perturbation degrees. In addition, users may now provide a gold-standard set to determine how enriched extracted pathways are with relevant genes compared to randomized versions of the original network.

摘要

识别功能模块或新的活性途径,最近被称为从头途径富集,是一项计算系统生物学挑战,在过去十年中受到了广泛关注。给定一个大型生物相互作用网络,关键途径挖掘器(KeyPathwayMiner)从编码为二元指标矩阵的一系列分子谱中提取富含差异活性实体的连通子网。由于相互作用网络不断演变,一个重要问题是当网络被修改时,提取结果的稳健性如何。我们通过几种网络扰动技术并在一系列扰动程度上,让用户能够研究这种效应。此外,用户现在可以提供一个金标准集,以确定与原始网络的随机版本相比,提取的途径与相关基因的富集程度如何。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/94ad/4965696/8a64cd4f828a/f1000research-5-9744-g0000.jpg

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