Continually Adaptive Machine Learning Platform for Personalized Biomedical Literature Curation and Exploration

用于个性化生物医学文献管理和探索的持续自适应机器学习平台

• 批准号: 10660315
• 负责人: TIMOTHY W CLARK
• 金额: $ 33.48万
• 依托单位: UNIVERSITY OF VIRGINIA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2027-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10660315
• 关键词: Acceleration; Adoption; Artificial Intelligence; Biomedical Research; Clinical; Communities; Complex; Custom; Data; Development; Dimensions; Environment; Evaluation; Expert Systems; Foundations; Goals; Individual; Knowledge; Laboratories; Learning; Literature; Machine Learning; Measures; Methods; Mission; Modeling; Morphologic artifacts; Navigation System; Printing; Process; Records; Reporting; Research; Research Personnel; Resources; Semantics; Services; Strategic Planning; Structure; System; Techniques; Time; Training; United States National Library of Medicine; Update; adaptive learning; deep learning; design; digital; flexibility; improved; interest; journal article; knowledge base; knowledge curation; learning strategy; natural language; next generation; novel; outreach; public repository; search engine; tool; web-based tool

Project Summary: This proposal develops a novel, continually adaptive learning, and web-based tool which automatically and continually integrates and curates biomedical literature and knowledge. It allows researchers and curators to continually integrate individual lab-generated articles with large-scale public repositories, thereby enabling biomedical users to perform custom and context-based integrative literature analysis and exploration. More specifically, the proposed framework proposes a novel knowledge-enriched representation learning model that continually interleaves the available biomedical corpora with the biomedical knowledge from domain-expert curated knowledge bases to generate a knowledge-enriched representation needed by curators to annotate biomedical journal articles, preprints, and clinical records with high accuracy and learning efficiency. Furthermore, the proposed framework builds an evolving multi-dimensional knowledge network that incorporates fresh knowledge into the knowledge network via dynamic updates. Such an up-to-date knowledge network facilitates perpetual exploration of biomedical literature and allows researchers and curators to fulfill their personalized information navigation needs quickly. Finally, the proposed web-based tool enables users to flexibly integrate their own/personalized articles and lab reports into the massive existing corpora and knowledge bases, query the up-to-date biomedical information landscapes, and drill down into aspects relevant to them in order to fulfill their specific analysis goals. Currently, there is no automatic or systematic method to continually integrate individual lab-generated articles with large-scale public repositories that enables biomedical researchers and curators to perform integrative analysis based on their own context and obtain biologically meaningful results. Thus, the proposed research is an important step to expedite the goal of automatically and continually curating entities from large-scale biomedical corpora, integrating valuable information from the community curated platforms, and designing an advanced navigation system that allows users to perform knowledge exploration for their specific information needs. Dissemination activities through tools will help promote the adoption of the proposed system into real-world laboratories and research environments, and beyond. Moreover, such a form of integration promotes great outreach and demonstrates our commitment to the FAIR principles, the ability to Find, Access, Interoperate, and Reuse digital content.

项目概要： 该提案开发了一种新颖的、持续自适应的学习和基于网络的工具，该工具可以自动和 不断整合和整理生物医学文献和知识。它允许研究人员和策展人 不断将各个实验室生成的文章与大型公共存储库相集成，从而使 生物医学用户执行定制和基于上下文的综合文献分析和探索。更多的 具体来说，所提出的框架提出了一种新颖的知识丰富的表示学习模型，该模型 不断地将可用的生物医学语料库与领域专家的生物医学知识相结合 策划的知识库，以生成策展人注释所需的知识丰富的表示 具有高精度和学习效率的生物医学期刊文章、预印本和临床记录。 此外，所提出的框架构建了一个不断发展的多维知识网络，其中包含 通过动态更新将新知识引入知识网络。这样一个最新的知识网络 促进对生物医学文献的永久探索，并允许研究人员和策展人实现他们的目标 个性化信息导航需要快捷。最后，所提出的基于网络的工具使用户能够灵活地 将他们自己的/个性化的文章和实验室报告整合到大量现有的语料库和知识库中， 查询最新的生物医学信息格局，并深入研究与之相关的方面，以便 实现他们的特定分析目标。目前，还没有自动或系统的方法来持续集成 具有大型公共存储库的单个实验室生成的文章，使生物医学研究人员和 策展人根据自己的背景进行综合分析并获得具有生物学意义的结果。 因此，所提出的研究是加快实现自动和持续策划目标的重要一步 来自大型生物医学语料库的实体，整合了社区策划的有价值的信息 平台，并设计先进的导航系统，允许用户进行知识探索 他们的具体信息需求。通过工具进行的传播活动将有助于促进 提议的系统进入现实世界的实验室和研究环境等。而且，这样的形式 一体化促进了广泛的外展活动，并展示了我们对公平原则的承诺、 查找、访问、互操作和重用数字内容。