DiseaseNet Finder: A Systems Medicine Toolkit for Clinical and Translational Rese

描述（由申请人提供）：许多复杂的人类疾病（如癌症、糖尿病、精神分裂症等）具有相应的复杂的、多基因的基因型，其启动和维持疾病进展。尽管在过去的几十年中取得了重大进展，确定基因介导的表型的关键，我们对复杂疾病的分子表型的功能基础的理解是不够的。由几个蛋白质以串行方式相互作用组成的信号通路过于简单化，并为多个相互作用的基因产物介导的行为提供了不充分的模型。通过对越来越多的注释良好的蛋白质-蛋白质相互作用（PPI）网络的严格研究部分地揭示，已经清楚的是，这些经典信号传导途径中的许多蛋白质参与与一组本体上不同的另外的蛋白质的“串扰”，并且被一组本体上不同的另外的蛋白质调节，其中这种串扰经常以组织和/或疾病特异性的方式介导。我们建议开发并提供一套集成的软件工具，以满足学术和商业研究界对定量PPI网络分析的未满足需求，从而推动实际的转化研究和验证。 DiseaseNet工具将在全球PPI网络中搜索和评分候选疾病子网络。它将允许多种高维组学类型（GWAS、SNP、CNV、蛋白质组学、miRNA等）的整合。与PPI网络，并包括分类工具。该软件的新颖方面包括：组合评分，多数据类型集成，节点和边缘预测工具，通过图形用户界面无缝实现端点分类和定量评分。 公共卫生相关性：复杂疾病包括许多基因与环境相互作用的贡献。需要增强的计算研究工具来发现生物标志物和理解复杂的疾病机制，以整合正在积累的各种类型的基因组学和蛋白质组学数据。这将使个性化医疗得到更快的发展。

DESCRIPTION (provided by applicant): Many complex human diseases (e.g. cancer, diabetes, schizophrenia etc.) have correspondingly complex, polygenic genotypes that initiate and sustain disease progression. Despite significant progress over the past few decades identifying genes critical to mediating phenotype, our understanding of the functional basis of molecular phenotype for complex diseases is insufficient. Signaling pathways that consist of a few proteins interacting in a serial fashion oversimplify and provide inadequate models for the behavior mediated by multiple interacting gene products. Partly revealed by rigorous studies of increasingly well-annotated protein-protein interaction (PPI) networks, it has become clear that many of the proteins in these canonical signaling pathways engage in "crosstalk" with, and are modulated by, an ontologically diverse set of additional proteins, where this crosstalk is frequently mediated in a tissue and/or disease specific manner. We propose to develop and deliver an integrated suite of software tools to the academic and commercial research community to fulfill the unmet demand for quantitative PPI network analysis that can drive practical translational research and validation. The tool DiseaseNet Finder will search for and score candidate disease sub- networks within global PPI networks. It will permit integration of multiple high- dimensional -omic types (GWAS, SNP, CNV, proteomic, miRNA etc.) with PPI networks and include classification tools. Novel aspects of the software include: combinatorial scoring, multi data type integration, node and edge prediction tools, with end-point classification and quantitative scoring seamlessly implemented through graphical user interfaces. PUBLIC HEALTH RELEVANCE: Complex diseases include the contributions of many genes interacting with the environment. Enhanced computational research tools to discover biomarkers and understand complex disease mechanisms are needed to integrate the various types of genomic and proteomics data that are accumulating. This will permit a more rapid development of personalized medicine.