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

> **NIH NIH R01** · UNIVERSITY OF VIRGINIA · 2024 · $334,276

## Abstract

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.

## Key facts

- **NIH application ID:** 10896301
- **Project number:** 5R01LM014012-02
- **Recipient organization:** UNIVERSITY OF VIRGINIA
- **Principal Investigator:** TIMOTHY W CLARK
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $334,276
- **Award type:** 5
- **Project period:** 2023-08-01 → 2027-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10896301

## Citation

> US National Institutes of Health, RePORTER application 10896301, Continually Adaptive Machine Learning Platform for Personalized Biomedical Literature Curation and Exploration (5R01LM014012-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10896301. Licensed CC0.

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