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

基本信息

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
项目类别：
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&apos 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

项目摘要

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 NIH普通基金（CF）计划生产了许多独特和高价值数据集。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取决于数据描述符跨CF程序，可能是不完整或不准确的。这些实验中的许多输出列表对某些生物医学条件显着的基因。我们建议使用这些基因列表来查找类似的数据集。这种方法不仅将启用跨CF数据集搜索，还可以连接它们可以在其他数据库和生物医学目录中进行其他实验，例如包含的数据库疾病基因的关联和分子途径。为了实现这一目标，我们将实施高效线性算法计算大量基因集之间的相似性。我们的原型工具， dbretina，使用此算法在几分钟内使用最小计算资源。 dbretina是Curindex的基础，研究相似性图连接多个与健康相关资源的数据库。 Dbretina和Curindex将允许高级搜索相关的CF实验并促进更好地解释生物医学数据。