Phenotype Discovery in NHLBI Genomic Studies (PhD)

摘要研究人员不断快速地将数据上传到公共存储库，但很少使用通用的注释标准，这使得跨研究的数据几乎不可能进行比较或关联。为了解决这个问题，我们将开发一个定义的元数据模型，并建立一个称为表型发现（PhD）的集成系统，使研究人员能够在公共存储库中查询和查找感兴趣的基因组研究，并以标准化的方式将新数据上传到我们的数据库（sdGaP）。查询解释器（Query Interpreter， QI）将利用文本挖掘和自然语言处理技术将自由文本映射到生物医学本体中的概念，从而有效地回答非结构化查询。在项目的第一阶段，我们将开发一个概念验证系统，该系统可以在dbGaP中回顾性地构建表型描述，并将与肺炎学领域的专家合作构建用例并评估自动映射。在二期项目中，我们将把该领域的专业知识扩展到心脏病学、血液学和睡眠障碍，以建立一个更全面的系统，将表型注释扩展到转录组数据库，并为sdGaP集成一个灵活的自动化基因型注释工具。我们将开发一个用户友好的界面，以协助研究人员上传标准化表型注释的数据。我们将从我们的网站免费提供该工具，并根据用户反馈和使用数据不断改进其质量。

DESCRIPTION (provided by applicant): Abstract Researchers continually upload data into public repositories at a rapid pace, yet utilize few common standards for annotation, making it close to impossible to compare or associate data across studies. To address this problem, we will develop a defined meta- data model and build an integrated system called Phenotype Discovery (PhD) that enables researchers to query and find genomic studies of interest in public repositories as well as upload new data into our database (sdGaP), in a standardized manner. A Query Interpreter (QI) will utilize text mining and natural language processing techniques to map free text into concepts in biomedical ontologies, allowing non-structured queries to be answered efficiently. In Phase I of the project, we will develop a proof-of-concept system that can retrospectively structure phenotypic descriptions in dbGaP, and will work with domain experts in pneumology to build use cases and evaluate the automated mappings. In Phase II of the project, we will extend the domain expertise to cardiology, hematology, and sleep disorders to build a more comprehensive system, expanding the phenotype annotation to transcriptome databases, and integrating a flexible automated genotype annotation tool for sdGaP. We will develop a user-friendly interface to prospectively assist researchers in uploading their data with standardized phenotypic annotations. We will provide the tool for free from our website and continuously improve its quality, based on user feedback and usage data. PUBLIC HEALTH RELEVANCE: Relevance Phenotype Discovery (PhD) represents a novel, automated system to describe the characteristics of patients whose genetic information is available in public data repositories, without compromising their privacy. This initiative is greatly needed so that more researchers can make use of data collected from projects funded by public agencies. PhD uses new methodology for natural language processing and semantic integration to interpret the narrative text as well as variables and their values from studies in genomic databases. Standardized terminologies will be utilized to ensure that data can be analyzed across different studies.