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

项目总结： 这一建议开发了一种新颖的、持续自适应的学习和基于Web的工具，该工具自动和 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 不断将实验室生成的单个文章与大型公共存储库集成，从而实现 生物医学用户执行基于定制和上下文的综合文献分析和探索。更多 具体地说，该框架提出了一种新颖的知识丰富的表征学习模型 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交织在一起 精选知识库，以生成馆长注释所需的丰富知识表示法 生物医学期刊论文、预印本和临床记录具有很高的准确性和学习效率。 此外，所提出的框架构建了一个不断演变的多维知识网络，该网络整合了 通过动态更新将新知识带入知识网络。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履行他们的 个性化的信息导航需求很快。最后，提出的基于Web的工具使用户能够灵活地 将他们自己/个性化的文章和实验室报告整合到现有的海量语料库和知识库中， 查询最新的生物医学信息景观，并深入到与其相关的方面，以便 实现其特定的分析目标。目前，还没有自动的或系统的方法来持续集成 具有大规模公共存储库的独立实验室生成的文章，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，拟议的研究是加速实现自动和持续管理目标的重要一步 来自大型生物医学语料库的实体，整合来自社区的有价值的信息 平台，并设计一个高级导航系统，允许用户执行知识探索 他们的特定信息需求。通过工具进行的传播活动将有助于促进采用 建议将该系统应用于真实世界的实验室和研究环境，以及更远的领域。而且，这样一种形式 融合促进了巨大的外展，并展示了我们对公平原则的承诺，有能力 查找、访问、互操作和重复使用数字内容。

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.