Ben-Hamo Rotem, Gidoni Moriah, Efroni Sol
The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan 5290002, Israel.
Bioinformatics. 2014 Sep 1;30(17):2399-405. doi: 10.1093/bioinformatics/btu199. Epub 2014 May 7.
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.
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.
Matlab code is available upon request.
Supplementary data are available at Bioinformatics online.
癌症转录组分析的核心挑战在于检测与疾病表型相关的分子差异。这种方法在识别分子特征以及将患者分层到临床组方面取得了显著进展。然而，尽管有这些进展，许多已识别的特征在临床上的实用性不足，且在分子机制的后续研究中缺乏足够的一致性。
为解决这些问题，我们引入了PhenoNet，这是一种用于识别与不同表型相关的通路和网络的新算法。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，并将这些数据与经过整理的通路和蛋白质 - 蛋白质相互作用信息整合。对所有可能的通路和子网络进行全面迭代，从而识别出区分两种表型的关键通路或子网络。
可根据请求提供Matlab代码。
补充数据可在《生物信息学》在线获取。
