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

DanceNet Finder：用于临床和转化研究的系统医学工具包

• 批准号: 8058237
• 负责人: Rod K Nibbe
• 金额: $ 15万
• 依托单位: NEOPROTEOMICS, INC.
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-03-15 至 2013-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8058237
• 关键词: Animal Model; Behavior; Benchmarking; Bioinformatics; Biological; Biological Markers; Biology; Businesses; Classification; Clinical; Clinical Research; Clinical Sciences; Clinical Trials; Communities; Complex; Computational Science; Computer software; Correlative Study; Data; Data Analyses; Development; Diabetes Mellitus; Disease; Disease Progression; Environment; Evaluation; Faculty; Gene Expression; Genes; Genomics; Genotype; Goals; Graph; Human; Malignant Neoplasms; Mediating; Medicine; MicroRNAs; Modeling; Mouse Protein; National Center for Research Resources; Ohio; Outcome; Pathway Analysis; Pharmaceutical Preparations; Phase; Phenotype; Proteins; Proteomics; Research; Research Personnel; Role; Scheme; Schizophrenia; Seeds; Services; Signal Pathway; Small Business Innovation Research Grant; Software Tools; Staging; System; Technology; Testing; Tissues; Toxic effect; Translational Research; Universities; Validation; Weight; analytical tool; base; combinatorial; commercialization; disease phenotype; experience; flexibility; genome wide association study; graphical user interface; human disease; improved; innovation; interest; molecular phenotype; novel; patient population; pre-clinical; programs; protein protein interaction; prototype; tool; user-friendly; web services

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.

描述（由申请人提供）：许多复杂的人类疾病（例如癌症、糖尿病、精神分裂症等）具有相应复杂的多基因基因型，这些基因型会启动和维持疾病进展。尽管过去几十年来在鉴定对介导表型至关重要的基因方面取得了重大进展，但我们对复杂疾病分子表型的功能基础的理解还不够。由一些以串行方式相互作用的蛋白质组成的信号通路过于简单化，并且为由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。对日益完善注释的蛋白质-蛋白质相互作用（PPI）网络的严格研究部分揭示，很明显，这些经典信号通路中的许多蛋白质与一组本体上不同的其他蛋白质进行“串扰”，并受其调节，其中这种串扰经常以组织和/或疾病特异性方式介导。我们建议向学术和商业研究界开发并提供一套集成的软件工具，以满足定量 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。 DrugsNet Finder 工具将在全球 PPI 网络中搜索候选疾病子网络并对其进行评分。它将允许将多种高维组学类型（GWAS、SNP、CNV、蛋白质组学、miRNA 等）与 PPI 网络集成，并包括分类工具。该软件的新颖之处包括：组合评分、多数据类型集成、节点和边缘预测工具，并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影响。需要增强的计算研究工具来发现生物标志物并了解复杂的疾病机制，以整合正在积累的各种类型的基因组和蛋白质组数据。这将使个性化医疗更快发展。