# Reactome IDG portal: Pathway-based analysis and visualization of understudied human proteins

> **NIH NIH U01** · OREGON HEALTH & SCIENCE UNIVERSITY · 2020 · $427,215

## Abstract

Project Summary
Proteins function through interactions with other proteins and biological entities to form biological
pathways inside and between cells. Targeted therapies are designed to mitigate or reverse malfunctions
caused by mutations in proteins via providing drugs to recover normal biological pathways’ activities.
Projecting understudied proteins in the context of biological pathways is a powerful way to infer potential
functions of these proteins.
Pathway-based approaches are now routinely applied in bioinformatics and computational biology data
analysis and visualization. Pathway databases are essential for those approaches. During the past two
decades, our team has been working together on building the Reactome knowledgebase, arguably the
most popular and comprehensive open source biological pathway database, covering over half of human
protein-coding genes and widely used in the research community.
In this application, we propose to develop a Reactome IDG pathway portal, which will allow localization
of understudied proteins in biological pathways, identifying likely interactions with better-known proteins
in specific processes annotated in Reactome, pinpointing most effective drug targets via pathway
modeling, thus generating testable predictions of molecular functions of these proteins in key domains of
biology. Specially we will develop a web-based application to place understudied proteins in the context
of Reactome pathways by importing a variety of data types collected in the IDG projects and other
resources and then overlaying them onto the Reactome pathways by leveraging existing Reactome
software tools (e.g. interaction overlay). Furthermore, we will develop a machine learning approach to
predict functional interactions between understudied proteins and well-known Reactome annotated
proteins and a Boolean network-based fuzzy logic modeling approach to integrate the scores produced
from the machine learning approach to simulate the impacts of understudied proteins on pathways’
activities.
We believe our approach will provide a unique and powerful approach to help the community to
understand the contribution of the understudied proteins to cellular functions.

## Key facts

- **NIH application ID:** 9904593
- **Project number:** 5U01CA239069-02
- **Recipient organization:** OREGON HEALTH & SCIENCE UNIVERSITY
- **Principal Investigator:** PETER G DEUSTACHIO
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $427,215
- **Award type:** 5
- **Project period:** 2019-04-01 → 2023-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9904593

## Citation

> US National Institutes of Health, RePORTER application 9904593, Reactome IDG portal: Pathway-based analysis and visualization of understudied human proteins (5U01CA239069-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9904593. Licensed CC0.

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