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: 9161233
• 负责人: CHRISTOPHER J MUNGALL
• 金额: $ 57.55万
• 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/9161233
• 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; Histology; Human Microbiome; Image; Intelligence; Knowledge; Learning; Logic; Machine Learning; Maintenance; Manuals; Measurement; Metadata; Ontology; Process; Research; Research Personnel; Research Project Grants; Sampling; Sea; Site; Source; Structure; Suggestion; System; Techniques; Testing; Text; Time; Training; Vision; Vocabulary; Work; base; dashboard; empowered; genome sequencing; improved; insight; meetings; microbial community; peer; repository; social; tool; transcriptomics; web portal

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的数据。这些数据各不相同 从基因组序列到表型测量和成像数据。如果研究人员 数据科学家可以有效地利用这些数据，然后我们可以深入了解疾病机制， 如何解决它们。然而，主要的绊脚石是，越来越难以找到和整合 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一个 关于某些微生物群落如何影响肠道组织学的关键数据集，因为缺乏描述性标签。 数据。目前，由于海量的数据，应用元数据是困难的、耗时的和容易出错的。 每种数据类型的混淆和重叠标准。通常雇用专门的“数据管理员”， 应用元数据，但即使是这些专家也因缺乏好的工具而受阻。在这里，我们建议开发一个 研究人员和数据管理员可以使用智能代理来帮助他们应用元数据。述试剂是 基于元数据元素的个性化仪表板，这些元数据元素可以从多个专门的 门户网站，以及维基百科等网站。这些元素可以与分类器耦合，分类器可以用于 自我识别可能与之相关的数据集，从而更容易选择适当的词汇表， 研究人员我们将为许多有针对性的用例部署系统，包括 国家生物医学信息中心生物样本储存库，以及 Figshare仓库。