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Using Published Pathway Figures in Enrichment Analysis and Machine Learning.

Min-Gyoung Shin, Alexander Pico

Biorxiv : the Preprint Server for Biology

|July 18, 2023

Summary

This summary is machine-generated.

Pathway Figure OCR (PFOCR) offers a comprehensive pathway database with detailed diagrams and literature support. It demonstrates superior coverage and unique advantages for cancer subtype and grade prediction.

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Area of Science:

Bioinformatics
Computational Biology
Systems Biology

Background:

Existing pathway databases vary in scope and detail.
There is a need for pathway resources that integrate visual, mechanistic information with literature support.
Published pathway figures are a rich, underutilized source of biological information.

Approach:

Pathway Figure OCR (PFOCR) extracts pathway data from published figures.
PFOCR content is compared against established pathway databases for coverage.
Advanced case studies explore PFOCR's utility in cancer subtype and grade prediction.

Key Points:

PFOCR provides a novel pathway database with extensive mechanistic diagrams and literature links.
The database approaches the breadth and depth of Gene Ontology.
PFOCR content is extracted from published pathway figures at a high rate.
PFOCR demonstrates competitive and in some cases superior coverage compared to existing databases.
PFOCR offers unique advantages for advanced analyses like cancer subtype and grade prediction.

Conclusions:

PFOCR represents a significant advancement in pathway database development.
Its approach provides rich, mechanistic insights and supports diverse analytical applications.
PFOCR enhances the utility of published pathway figures for biological research and precision medicine.