Akhmedov Murodzhon, Kedaigle Amanda, Chong Renan Escalante, Montemanni Roberto, Bertoni Francesco, Fraenkel Ernest, Kwee Ivo
Dalle Molle Institute for Artificial Intelligence Research (USI/SUPSI), Manno, Switzerland.
Institute of Oncology Research (IOR), Bellinzona, Switzerland.
PLoS Comput Biol. 2017 Jul 31;13(7):e1005694. doi: 10.1371/journal.pcbi.1005694. eCollection 2017 Jul.
With the recent technological developments a vast amount of high-throughput data has been profiled to understand the mechanism of complex diseases. The current bioinformatics challenge is to interpret the data and underlying biology, where efficient algorithms for analyzing heterogeneous high-throughput data using biological networks are becoming increasingly valuable. In this paper, we propose a software package based on the Prize-collecting Steiner Forest graph optimization approach. The PCSF package performs fast and user-friendly network analysis of high-throughput data by mapping the data onto a biological networks such as protein-protein interaction, gene-gene interaction or any other correlation or coexpression based networks. Using the interaction networks as a template, it determines high-confidence subnetworks relevant to the data, which potentially leads to predictions of functional units. It also interactively visualizes the resulting subnetwork with functional enrichment analysis.
随着近期技术的发展，大量高通量数据已被剖析以了解复杂疾病的机制。当前生物信息学面临的挑战是解读数据及潜在生物学信息，在此过程中，利用生物网络分析异构高通量数据的高效算法正变得越来越有价值。在本文中，我们提出了一个基于带权收集斯坦纳森林图优化方法的软件包。PCSF软件包通过将高通量数据映射到诸如蛋白质-蛋白质相互作用、基因-基因相互作用或任何其他基于相关性或共表达的网络等生物网络上，对高通量数据进行快速且用户友好的网络分析。以相互作用网络为模板，它确定与数据相关的高可信度子网，这可能会带来功能单元的预测。它还通过功能富集分析以交互方式可视化所得的子网。
