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中文摘要
翻译
描述(由申请人提供):不断产生的丰富的生物和生物医学数据保证了生命科学的巨大进步。为了实现这一承诺,需要对这一迅速扩大的信息库进行有效整合,也就是说,以一种可以查询的方式进行组合,以提取相关数据,然后对这些数据进行分析,以回答有意义的研究问题。本提案的主要目标是开发GeneTegra系统,这是一个信息集成解决方案,提供了一个通用的交互环境来查询来自多个来源的数据和知识。为了有效地整合来自不同数据源的知识,必须克服两个主要障碍:语法异质性,其中数据源具有不同的表示和访问机制;语义可变性,相似的词汇可能指的是多个概念,或者不同的词汇指的是同一个概念。GeneTegra系统通过使用语义Web技术解决了这些障碍:使用Web本体语言(OWL)构建的本体作为不同格式数据源的公共数据和知识表示,生成和维护这些本体表示的自动化机制,以及基于可重用的、面向服务的中介的健壮的系统体系结构。所提议的系统的核心由项目第一阶段开发的通用算法、过程和机制组成,这些算法、过程和机制支持本体的自动生成、本体模型之间语义对应的自动识别,以及对这些本体建模、分布式、异构源的查询的创建和执行。在第二阶段,GeneTegra系统将作为基于第一阶段开发的核心组件的以人为本的解决方案进行开发、实施和测试,包括用于查询创建和执行的高度可用的接口,使用Web服务标准的注册、共享和重用信息的机制,确定数据质量和查询可靠性的机制。安全和隐私子系统允许构建协作社区,同时确保通过系统访问信息的用户经过适当的身份验证和授权。GeneTegra系统的设计和评估将专门用于整合与基因型-表型关联调查相关的资源,以及鉴定与人类疾病和病症有关的基因。GeneTegra系统是一个信息集成解决方案,它提供了一个通用的交互环境来查询来自多个异构源的数据和知识。它使用本体作为语义和语法建模的基本公式,并包含用于生成这些本体以及用于重用和共享集成配置的自动化机制。它专门用于解决与基因型-表型关联调查相关的来源的综合查询,以及识别导致人类疾病和病症的基因。
英文摘要
DESCRIPTION (provided by applicant): The wealth of biological and biomedical data constantly being generated promises dramatic advancement in the life sciences. To realize this promise, this pool of rapidly expanding information needs to be efficiently integrated, that is, combined in such a way that it can be queried to extract relevant data that can be subsequently analyzed to answer meaningful research questions. The main objective of this proposal is to develop the GeneTegra System, an information integration solution that provides a common interaction environment to query data and knowledge from multiple sources. Two main obstacles have to be overcome in order to attain an effective integration of knowledge from different data sources: syntactic heterogeneity, where data sources have different representation and access mechanisms; and semantic variability, where similar lexical terms may refer to multiple concepts or dissimilar terms refer to the same concept. The GeneTegra System addresses these obstacles through the use of Semantic Web technologies: ontologies constructed using the Web Ontology Language (OWL) as a common data and knowledge representation for data sources of diverse formats, automated mechanisms for the generation and maintenance of these ontology representations, and a robust system architecture based on reusable, service-oriented mediators. The core of the proposed system consists of general algorithms, procedures, and mechanisms developed during Phase I of this project, that enable the automatic generation of ontologies, the automated identification of semantic correspondences between ontology models, and the creation and execution of queries over these ontology- modeled, distributed, heterogeneous sources. In Phase II, the GeneTegra System will be developed, implemented, and tested as a human-centered solution building on the core components developed during Phase I, incorporating a highly usable interface for query creation and execution, a mechanism for registration, sharing, and re-use of information using Web Services standards, a mechanism for determining quality of data and query reliability, and a security and privacy subsystem that allows the construction of collaborative communities while ensuring that users are properly authenticated and authorized to access information through the system. The GeneTegra System will be designed and evaluated to specifically address the integration of sources relevant to investigations of genotype-phenotype associations and to the identification of genes responsible for human diseases and conditions. PUBLIC HEALTH RELEVANCE The GeneTegra System is an information integration solution that provides a common interaction environment to query data and knowledge from multiple heterogeneous sources. It uses ontologies as the base formulism for semantic and syntactic modeling, and contains automated mechanisms for the generation of these ontologies, and for the reuse and sharing of integration configurations. It is specifically designed to address the integrated querying of sources relevant to investigations of genotype-phenotype associations and to the identification of genes responsible for human diseases and conditions.
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Deep-CDS: Deep Learning Semantic Data Lake for Clinical Decision Support
  • 批准号:
    10546333
  • 项目类别:
  • 资助金额:
    $25.35万
  • 财政年份:
    2022
  • 负责人:
    Mansur R. Kabuka
  • 依托单位:
Deep-CDS: Deep Learning Semantic Data Lake for Clinical Decision Support
  • 批准号:
    10747223
  • 项目类别:
  • 资助金额:
    $71.43万
  • 财政年份:
    2022
  • 负责人:
    Mansur R. Kabuka
  • 依托单位:
Microbiome Meta-Analysis Platform
  • 批准号:
    10011865
  • 项目类别:
  • 资助金额:
    $29.38万
  • 财政年份:
    2017
  • 负责人:
    Mansur R. Kabuka
  • 依托单位:
Semantic Data Lake for Biomedical Research
  • 批准号:
    9536289
  • 项目类别:
  • 资助金额:
    $58.97万
  • 财政年份:
    2016
  • 负责人:
    Mansur R. Kabuka
  • 依托单位:
海外基金