Phenotype Discovery in NHLBI Genomic Studies (PhD)

NHLBI 基因组研究中的表型发现（博士）

• 批准号: 8145134
• 负责人: LUCILA OHNO-MACHADO
• 金额: $ 54.03万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-07-19 至 2013-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8145134
• 关键词: Address; Bioinformatics; Cardiology; Characteristics; Collaborations; Computer software; Data; Databases; Deposition; Dictionary; Ensure; Environment; Feedback; Funding; Gene Expression Profile; Genetic; Genomics; Genotype; Goals; Hematology; Informatics; Learning; Lung diseases; Maps; Methodology; Methods; National Heart, Lung, and Blood Institute; Natural Language Processing; Online Systems; Ontology; Patients; Phase; Phenotype; Postdoctoral Fellow; Privacy; Protocols documentation; Pulmonology; Research; Research Personnel; Scientist; Semantics; Sleep Disorders; Source; Structure; System; Techniques; Technology; Terminology; Text; Training; Work; abstracting; base; biomedical informatics; biomedical ontology; data modeling; database of Genotypes and Phenotypes; flexibility; improved; interest; novel; programs; prototype; repository; study characteristics; text searching; tool; user-friendly; web site

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.

描述(由申请人提供)：抽象研究人员不断快速地将数据上传到公共存储库，但几乎没有使用通用标准进行注释，这使得几乎不可能在不同研究之间比较或关联数据。为了解决这个问题，我们将开发一个定义的元数据模型，并建立一个名为表型发现(PhD)的集成系统，使研究人员能够以标准化的方式查询和查找公共存储库中感兴趣的基因组研究，并将新数据上传到我们的数据库(SdGaP)。查询解释器(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，从而允许高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中回溯地构建表型描述，并将与肺病学领域的专家合作来构建用例并评估自动映射。在项目的第二阶段，我们将把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，并集成灵活的sdGaP自动基因注释工具。我们将开发一个用户友好的界面，以前瞻性地帮助研究人员上传带有标准化表型注释的数据。我们将根据用户反馈和使用数据，从我们的网站上免费提供该工具，并不断提高其质量。 公共卫生相关性：相关性表型发现(PHD)代表了一种新颖的自动化系统，用于描述患者的特征，这些患者的遗传信息在公共数据库中可用，而不会损害他们的隐私。这一倡议非常必要，以便更多的研究人员能够利用从公共机构资助的项目中收集的数据。博士使用自然语言处理和语义整合的新方法来解释叙述文本以及来自基因组数据库研究的变量及其值。将使用标准化术语，以确保可以跨不同研究分析数据。