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

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.