Data-driven search of Common Fund data sets for better discoverability and novel meta-analysis

对共同基金数据集进行数据驱动搜索，以实现更好的可发现性和新颖的荟萃分析

• 批准号: 10577377
• 负责人: Tamer Ahmed Mansour Ahmed
• 金额: $ 31.3万
• 依托单位: UNIVERSITY OF CALIFORNIA AT DAVIS
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-20 至 2024-09-19
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10577377
• 关键词: Address; Adopted; Algorithms; Alzheimer' s Disease; Animal Model; Catalogs; Child; Collaborations; Collection; Complex; Computer software; Data; Data Set; Data Sources; Databases; Descriptor; Development; Disease; Exercise; Foundations; Funding; Generations; Genes; Genotype; Genotype-Tissue Expression Project; Grant; Graph; Health; International; Malignant Neoplasms; Meta-Analysis; Metadata; Modeling; Molecular; Mus; Network-based; Output; Overlapping Genes; Pathway interactions; Phenotype; Physical activity; Privatization; Recipe; Research; Resources; Syndrome; Techniques; Testing; Tissues; Transducers; United States National Institutes of Health; base; cancer predisposition; cohort; computing resources; congenital anomaly; database query; experience; experimental study; flexibility; human disease; insight; interest; mouse model; multiple datasets; novel; novel strategies; programs; prototype; repository; software development; tool; usability; user-friendly

Project Summary NIH Common Fund (CF) programs have produced a number of unique and high-value data sets. To solve complex biomedical questions, we need to find related data sets that can be co-analyzed for specific study purposes. Many of the current search techniques depend on data descriptors which differ across CF programs and may be incomplete or inaccurate. Many of these experiments output lists of genes significant to certain biomedical conditions. We are proposing to use these gene lists to find similar data sets. This approach will not only enable searching across CF data sets but also can connect them to other experiments in other databases and biomedical catalogs, e.g., databases containing disease-gene associations and molecular pathways. To achieve this aim, we will implement an efficient linear algorithm to calculate similarities between large numbers of gene sets. Our prototype tool, DBRetina, uses this algorithm to build huge similarity networks in few minutes using minimal computational resources. DBRetina serves as the foundation for CurIndex, a study similarity graph database that connects multiple health-related resources. DBRetina and CurIndex will allow advanced search for related CF experiments and facilitate better interpretation of biomedical data.

项目摘要 美国国立卫生研究院共同基金(CF)计划产生了许多独特和高价值的数据集。 为了解决复杂的生物医学问题，我们需要找到可以共同分析的相关数据集 具体的研究目的。当前的许多搜索技术依赖于不同的数据描述符 跨CF计划，可能不完整或不准确。这些实验中的许多都输出了 对某些生物医学条件有重要意义的基因。我们建议使用这些基因列表来寻找 相似的数据集。这种方法不仅可以跨CF数据集进行搜索，还可以连接 用于其他数据库和生物医学目录中的其他实验，例如，包含 疾病-基因关联和分子途径。为了实现这一目标，我们将实施有效的 计算大量基因集合之间相似性的线性算法。我们的原型工具， DBRetina，使用这种算法在几分钟内建立巨大的相似网络，使用最少的 计算资源。DBRetina是研究相似性图CurIndex的基础 连接多个健康相关资源的数据库。DBRetina和CurIndex将允许高级 搜索相关的CF实验，帮助更好地解释生物医学数据。