BackgroundThe availability of large-scale curated protein interaction datasets has given rise to the opportunity to investigate higher level organization and modularity within the protein interaction network (PPI) using graph theoretic analysis. Despite the recent progress, systems level analysis of PPIS remains a daunting task as it is challenging to make sense out of the deluge of high-dimensional interaction data. Specifically, techniques that automatically abstract and summarize PPIS at multiple resolutions to provide high level views of its functional landscape are still lacking. We present a novel data-driven and generic algorithm called FUSE (>Functional >Summary G>enerator) that generates functional maps of a PPI at different levels of organization, from broad process-process level interactions to in-depth complex-complex level interactions, through a pro t maximization approach that exploits Minimum Description Length (MDL) principle to maximize information gain of the summary graph while satisfying the level of detail constraint.
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机译：背景技术大规模策划的蛋白质相互作用数据集的可用性为利用图论分析研究蛋白质相互作用网络（PPI）中更高层次的组织和模块性提供了机会。尽管取得了最新进展，但是PPIS的系统级分析仍然是一项艰巨的任务，因为要从大量的高维交互数据中弄清楚这一点具有挑战性。具体而言，仍然缺乏以多种分辨率自动提取和摘要PPIS以提供其功能概况的高级视图的技术。我们提出了一种新颖的数据驱动型通用算法，称为FUSE（> Fu nc > S 摘要G > e 生成器），该算法在以下位置生成PPI的功能图组织的不同级别，从广泛的流程到流程级别的交互，再到复杂的复杂级别的深入交互，都通过使用最小描述长度（MDL）原理的摘要最大化方法来最大化摘要图的信息增益，同时又满足细节约束。
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