Semantic Data Lake for Biomedical Research

用于生物医学研究的语义数据湖

• 批准号: 9765194
• 负责人: Mansur R. Kabuka
• 金额: $ 23.88万
• 依托单位: INFOTECH SOFT, INC.
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-15 至 2020-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9765194
• 关键词: Acute; Address; Adoption; Area; Big Data; Biomedical Computing; Biomedical Research; Catalogs; Chronic Myeloid Leukemia; Clinical; Clinical Trials; Collection; Colorectal Cancer; Communities; Complex; Computer software; Data; Data Analyses; Data Analytics; Data Collection; Data Discovery; Data Quality; Data Science; Data Set; Data Sources; Demographic Factors; Development; Electronic Health Record; Ensure; Environment; Environmental Risk Factor; Evaluation; Explosion; Generations; Genetic; Genetic Markers; High-Throughput Nucleotide Sequencing; Individual; Informatics; Ingestion; Institution; Intelligence; Knowledge; Knowledge Extraction; Legal; Legal patent; Liquid substance; Machine Learning; Malignant Neoplasms; Methods; Modeling; Monitor; Non-Small-Cell Lung Carcinoma; Online Systems; Ontology; Phase; Policies; Precision therapeutics; Procedures; Process; Production; Productivity; Recommendation; Research; Research Personnel; Research Project Grants; Resources; Retrieval; Risk; Secure; Security; Semantics; Services; Source; Specific qualifier value; Structure; System; Technology; Testing; Vocabulary; Work; base; cancer therapy; clinical data warehouse; cluster computing; computer based Semantic Analysis; cost effective; data access; data exchange; data integration; data management; data modeling; data resource; data warehouse; design; disease heterogeneity; experience; genetic information; handheld mobile device; indexing; individualized medicine; melanoma; migration; natural language; new technology; next generation; novel; off-patent; precision medicine; prototype; success; systems research; targeted treatment; technology development; time use; tool

Capitalizing on the transformative opportunities afforded by the extremely large and ever-growing volume, velocity, and variety of biomedical data being continuously produced is a major challenge. The development and increasingly widespread adoption of several new technologies, including next generation genetic sequencing, electronic health records and clinical trials systems, and research data warehouses means that we are in the midst of a veritable explosion in data production. This in turn results in the migration of the bottleneck in scientific productivity into data management and interpretation: tools are urgently needed to assist cancer researchers in the assembly, integration, transformation, and analysis of these Big Data sets. In this project, we propose to develop the Semantic Data Lake for Biomedical Research (SDL-BR) system, a cluster-computing software environment that enables rapid data ingestion, multifaceted data modeling, logical and semantic querying and data transformation, and intelligent resource discovery. SDL-BR is based on the idea of a data lake, a distributed store that does not make any assumptions about the structure of incoming data, and that delays modeling decisions until data is to be used. This project adds to the data lake paradigm methods for semantic data modeling, integration, and querying, and for resource discovery based on learned relationships between users and data resources.

如何利用不断产生的巨大且不断增长的数量、速度和种类的生物医学数据所带来的变革机遇是一项重大挑战。多项新技术的开发和日益广泛的采用，包括下一代基因测序、电子健康记录和临床试验系统以及研究数据 仓库意味着我们正处于数据生产的真正爆炸之中。这反过来导致科学生产力的瓶颈转移到数据管理和解释上：迫切需要工具来协助癌症研究人员组装、集成、转换和分析这些大数据集。在这个项目中，我们建议开发语义 生物医学研究数据湖 (SDL-BR) 系统是一个集群计算软件环境，可实现快速数据摄取、多方面数据建模、逻辑和语义查询和数据转换以及智能资源发现。 SDL-BR 基于数据湖的思想，这是一种分布式存储，不对传入数据的结构做出任何假设，并且 延迟建模决策，直到使用数据为止。该项目添加了数据湖范式方法，用于语义数据建模、集成和查询，以及基于用户和数据资源之间学习关系的资源发现。