# Image-guided Biocuration of Disease Pathways From Scientific Literature

> **NIH NIH R01** · UNIVERSITY OF MISSOURI-COLUMBIA · 2020 · $313,495

## Abstract

Realization of precision medicine ideas requires an unprecedented rapid pace of translation of biomedical
discoveries into clinical practice. However, while many non-canonical disease pathways and uncommon drug
actions, which are of vital importance for understanding individual patient-specific disease pathways, are
accumulated in the literature, most are not organized in databases. Currently, such knowledge is curated
manually or semi-automatically in a very limited scope. Meanwhile, the volume of biomedical information in
PubMed (currently 28 million publications) keeps growing by more than a million articles per year, which
demands more efficient and effective biocuration approaches.
 To address this challenge, a novel biocuration method for automatic extraction of disease pathways from
figures and text of biomedical articles will be developed.
 Specific Aim 1: To develop focused benchmark sets of articles to assess the performance of the biocuration
pipeline.
 Specific Aim 2: To develop a method for extraction of components of disease pathways from articles’ figures
based on deep-learning techniques.
 Specific Aim 3: To develop a method for reconstruction of disease-specific pathways through enrichment
and through graph neural network (GNN) approaches.
 Specific Aim 4: To conduct a comprehensive evaluation of the pipeline.
 The overarching goal of this project is to develop a computer-based automatic biocuration ecosystem for
rapid transformation of free-text biomedical literature into a machine-processable format for medical
applications.
 The overall impact of the proposed project will be to significantly improve health outcomes in
individualized patient cases by efficiently bringing the latest biomedical discoveries into a precision
medicine setting. It will especially benefit cancer patients for which up-to-date knowledge of newly
discovered molecular mechanisms and drug actions is critical.

## Key facts

- **NIH application ID:** 9987133
- **Project number:** 1R01LM013392-01
- **Recipient organization:** UNIVERSITY OF MISSOURI-COLUMBIA
- **Principal Investigator:** Mihail Popescu
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $313,495
- **Award type:** 1
- **Project period:** 2020-05-01 → 2024-02-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9987133

## Citation

> US National Institutes of Health, RePORTER application 9987133, Image-guided Biocuration of Disease Pathways From Scientific Literature (1R01LM013392-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9987133. Licensed CC0.

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