PhenoNet: identification of key networks associated with disease phenotype
PhenoNet: identification of key networks associated with disease phenotype
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DOI：
10.1093/bioinformatics/btu199
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发表时间：
2014-09-01
期刊：
影响因子：
5.8
通讯作者：
Efroni, Sol
中科院分区：
文献类型：
--
作者：
Ben-Hamo, Rotem;Gidoni, Moriah;Efroni, Sol
动机：癌症转录组分析的核心是检测与疾病表型相关的分子差异的挑战。这种方法在识别分子特征和将患者分为临床组方面取得了显著进展。然而，尽管取得了这一进展，许多已确定的签名是不够强大的临床使用和不一致的，足以提供后续的分子mechanism.Results：为了解决这些问题，我们引入PhenoNet，一种新的算法，用于识别与不同表型相关的通路和网络。PhenoNet使用两种类型的输入数据：基因表达数据（RMA，RPKM，FPKM等）和表型信息，并整合这些数据与策划的途径和蛋白质-蛋白质相互作用的信息。在所有可能的途径和子网络中进行全面的迭代，可以识别出区分两种表型的关键途径或子网络。
Motivation: At the core of transcriptome analyses of cancer is a challenge to detect molecular differences affiliated with disease phenotypes. This approach has led to remarkable progress in identifying molecular signatures and in stratifying patients into clinical groups. Yet, despite this progress, many of the identified signatures are not robust enough to be clinically used and not consistent enough to provide a follow-up on molecular mechanisms.Results: To address these issues, we introduce PhenoNet, a novel algorithm for the identification of pathways and networks associated with different phenotypes. PhenoNet uses two types of input data: gene expression data (RMA, RPKM, FPKM, etc.) and phenotypic information, and integrates these data with curated pathways and protein-protein interaction information. Comprehensive iterations across all possible pathways and subnetworks result in the identification of key pathways or subnetworks that distinguish between the two phenotypes.