Integration and Visualization of Diverse Biological Data

多种生物数据的整合与可视化

• 批准号: 9266422
• 负责人: OLGA G TROYANSKAYA
• 金额: $ 44.53万
• 依托单位: PRINCETON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-04-01 至 2019-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9266422
• 关键词: Address; Affect; Algorithms; Animal Model; Area; Bioinformatics; Biological; Biological Process; Cardiovascular Diseases; Case Study; Cell Lineage; Chronic Kidney Failure; Clinical; Collaborations; Complex; Computer Systems; Computing Methodologies; Data; Data Analyses; Data Set; Development; Disease; Drug Targeting; Expert Systems; Feedback; Functional disorder; Funding; Gene Expression Regulation; Generations; Genes; Genetic Databases; Genetic study; Goals; Gold; Hereditary Disease; Human; Hypertension; Imagery; Kidney Glomerulus; Knowledge; Label; Laboratories; Learning; Letters; Link; Machine Learning; Methodology; Methods; Modeling; Molecular; Nephrology; Network-based; Parkinson Disease; Pathway interactions; Quantitative Genetics; Real-Time Systems; Research; Research Personnel; Scientist; Signal Transduction; Substantia nigra structure; Supervision; System; Systems Integration; Time; Tissues; Training; Ubiquitination; Update; Work; autism spectrum disorder; base; clinical investigation; data integration; data visualization; diagnostic biomarker; drug development; drug discovery; experimental study; functional genomics; gene discovery; genome wide association study; genome-wide; genomic data; human disease; improved; innovation; insight; interest; multitask; novel; novel therapeutics; public health relevance; targeted treatment; therapy development; user-friendly; web interface; web-accessible

 DESCRIPTION (provided by applicant): The onset of most human disease involves multiple, molecular-level changes to the complex system of interacting genes and pathways that function differently in specific cell-lineage, pathway and treatment contexts. While this system has been probed by the thousands of functional genomics and quantitative genetic studies, careful extraction of signals relevant to these specific contexts is a challenging problem. General integration of these heterogeneous data was an important first step in detecting signals that be used to build networks to generate experimentally-testable hypotheses. However, only by dealing with the fact that disease happens at the intersection of multiple contexts and by integrating functional genomics with quantitative genetics will we be able to move toward a molecular-level understanding of human pathophysiology, which will pave the way to new therapy and drug development. The long-term goal of this project is to enable such discoveries through the development of innovative bioinformatics frameworks for integrative analysis of diverse functional genomic data. In the previous funding periods, we developed accurate data integration and visualization methodologies for most common model organisms and human, created methods for tissue-specific data analysis, and applied these methods to make novel insights about important biological processes. We further enabled experimental biological discovery by implementing these methods in publicly accessible interactive systems that are popular with experimental biologists. Leveraging our prior work, we now will directly address the challenge of enabling data-driven study of molecular mechanisms underlying human disease by developing novel semi-supervised and multi-task machine learning approaches and implementing them in a real-time integration system capable of predicting genome-scale functional and mechanism-specific networks focused on any biological context of interest. This will allow any biomedical researcher to quickly make data-driven hypotheses about function, interactions, and regulation of genes involved in hypertension in the kidney glomerulus or to predict new regulatory interactions relevant to Parkinson's disease that affect the ubiquitination pathway in Substantia nigra. Furthermore, we will develop methods for disease gene discovery that leverage these highly specific networks for functional analysis of quantitative genetics data. Our deliverable will be a general, robust, user-friendly, and automatically updated system for user-driven functional genomic data integration and functional analysis of quantitative genetics data. Throughout this work, we (with our close experimental and clinical collaborators) will also apply our methods to chronic kidney disease, cardiovascular disease/hypertension, and autism spectrum disorders both as case studies for the iterative improvement of our methods and to make direct contribution to better understanding of these diseases.

 描述（由适用提供）：大多数人类疾病的发作涉及多个分子级的变化，以相互作用的基因和途径的复杂系统变化，这些系统在特定的细胞限制，途径和治疗环境中的作用不同。尽管该系统已通过数千种功能基因组学和定量遗传研究证明，但仔细提取与这些特定情况相关的信号是一个挑战问题。这些异质数据的一般整合是检测用于构建网络以生成实验检验的假设的信号的重要第一步。但是，只有通过处理以下事实：疾病发生在多种环境的交集以及将功能基因组学与定量遗传学结合在一起，我们才能朝着对人类病理生理学的分子水平理解迈进，这将为新疗法和药物发育铺平道路。该项目的长期目标是通过开发创新的生物信息学框架来实现此类发现，以综合分析多样性的功能基因组数据。在前面的资金期间，我们为大多数常见的模型生物和人类开发了准确的数据整合和可视化方法，为组织特异性数据分析创建了方法，并应用了这些方法来对重要的生物学过程进行新的见解。我们通过在实验生物学家流行的公共交互系统中实施这些方法，进一步启用了实验生物学发现。利用我们先前的工作，我们现在将直接解决通过开发新型的半监督和多任务机器学习方法来实现数据驱动的分子机制研究的挑战，并在实时集成系统中实施它们，能够预测基因组规模尺度的功能和机制特定网络，这些网络侧重于任何感兴趣的生物学环境。这将使任何生物医学研究人员都可以迅速提出有关肾肾小球高血压的功能，相互作用和调节的数据驱动的假设，或者预测与帕金森氏病有关的新调节相互作用，这些相互作用会影响棘皮动物的泛素化途径。此外，我们将开发疾病基因发现方法，这些方法利用这些高度特定的网络来进行定量基因数据的功能分析。 我们的可交付方式将是一个通用，健壮，用户友好且自动更新的系统，用于用户驱动的功能基因组数据集成和定量遗传学数据的功能分析。通过这项工作，我们（凭借我们的近距离实验和临床合作者）还将将我们的方法应用于慢性肾脏疾病，心血管疾病/高血压和自闭症谱系障碍，既可以作为案例研究，以改善我们的方法，并为更好地理解这些疾病做出直接贡献。