An Intelligent Concept Agent for Assisting with the Application of Metadata

辅助元数据应用的智能概念代理

• 批准号: 9357656
• 负责人: CHRISTOPHER J MUNGALL
• 金额: $ 57.28万
• 依托单位: UNIVERSITY OF CALIF-LAWRENC BERKELEY LAB
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-23 至 2019-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9357656
• 关键词: Address; Affect; Area; Categories; Classification; Collaborations; Collection; Communities; Complex; Coupled; Crohn' s disease; Data; Data Science; Data Set; Databases; Deposition; Disease; Distributed Systems; Ecosystem; Elements; Environment; Fostering; Frustration; Genome; Histology; Human Microbiome; Image; Intelligence; Internet; Knowledge; Learning; Logic; Machine Learning; Maintenance; Manuals; Measurement; Metadata; Ontology; Phenotype; Research; Research Personnel; Research Project Grants; Sampling; Sea; Site; Source; Structure; Suggestion; System; Techniques; Testing; Text; Time; Training; Vision; Vocabulary; base; dashboard; improved; insight; microbial community; peer; prospective; repository; social; tool; transcriptomics

PROJECT ABSTRACT Biomedical investigators are generating increasing amounts of complex and diverse data. This data varies tremendously, from genome sequences through phenotypic measurements and imaging data. If researchers and data scientists can tap into this data effectively, then we can gain insights into disease mechanisms and how to tackle them. However, the main stumbling block is that it is increasingly hard to find and integrate the relevant datasets due to the lack of sufficient metadata. A researcher studying Crohn's disease may miss a crucial dataset on how certain microbial communities affect gut histology due to the lack of descriptive tags on the data. Currently, applying metadata is difficult, time-consuming and error prone due to the vast sea of confusing and overlapping standards for each datatype. Often specialized `data wranglers' are employed to apply metadata, but even these experts are hindered by lack of good tools. Here we propose to develop an intelligent agent that researchers and data wranglers can use to assist them apply metadata. The agent is based around a personalized dashboard of metadata elements that can be collected from multiple specialized portals, as well as sites such as Wikipedia. These elements can be coupled with classifiers that can be used to self-identify datasets to which they may be relevant, making the selection of appropriate vocabularies easier for researchers. We will deploy the system for a number of targeted use cases, including annotation of the National Center for Biomedical Information Bio-Samples repository, and annotation of images within the Figshare repository.

项目摘要 生物医学研究人员正在产生越来越多的复杂和多样化数据。此数据有所不同 从基因组序列到表型测量和成像数据，巨大的意义。如果研究人员 数据科学家可以有效利用这些数据，然后我们可以深入了解疾病机制和 如何解决他们。但是，主要的绊脚石是越来越难以找到和集成 由于缺乏足够的元数据，相关数据集。研究克罗恩病的研究人员可能会错过 关于某些微生物群落如何影响肠道组织学如何由于缺乏描述性标签而影响肠道组织学的关键数据集 数据。目前，应用元数据很困难，由于广阔的海洋，耗时和错误 每个数据类型的混淆和重叠标准。通常使用专业的“数据纠缠者” 应用元数据，但即使这些专家也由于缺乏好工具而受到阻碍。在这里，我们建议开发 研究人员和数据牧马人可以用来帮助他们应用元数据的智能代理。代理是 基于可以从多个专业化的元数据元素的个性化仪表板 门户以及Wikipedia等站点。这些元素可以与分类器相结合，可用于 自我识别可能与之相关的数据集，使选择合适的词汇更容易 研究人员。我们将在许多有针对性的用例中部署系统，包括 国家生物医学信息中心生物样本存储库，以及图像的注释 Figshare存储库。