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Updated: Jun 18, 2025

A Web Tool for Generating High Quality Machine-readable Biological Pathways

A Web Tool for Generating High Quality Machine-readable Biological Pathways

Published on: February 8, 2017

BPP: a platform for automatic biochemical pathway prediction.

Xinhao Yi¹, Siwei Liu², Yu Wu³

¹School of Computing Science, University of Glasgow, 18 Lilybank Gardens, Glasgow G12 8RZ, United Kingdom.

Briefings in Bioinformatics

|July 31, 2024

Summary

This summary is machine-generated.

This study introduces the biochemical pathway prediction (BPP) platform, utilizing representation learning and hypergraph neural networks to identify links in biochemical pathways. It offers an automated approach to predict reaction participants and products, overcoming experimental cost limitations.

Keywords:

biological networks graph neural networks hypergraphs pathway bioinformatics

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A Web Tool for Generating High Quality Machine-readable Biological Pathways

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Published on: November 3, 2011

Area of Science:

Biochemistry and Bioinformatics
Computational Biology
Systems Biology

Background:

Biochemical pathways are crucial for understanding life processes, involving complex networks of reactions.
Current methods for studying these pathways, primarily experimental and database-driven, face significant cost constraints.
Representation learning offers a promising alternative for analyzing complex biological networks.

Purpose of the Study:

To develop an automated platform for biochemical pathway prediction (BPP).
To identify potential links, reactants, and products within biochemical pathway networks.
To overcome the limitations of traditional experimental and database analysis methods.

Main Methods:

Developed the biochemical pathway prediction (BPP) platform incorporating various representation learning models.
Utilized hypergraph neural networks to model biochemical reactions.
Integrated novel biochemical pathway datasets and an SHAP explainer for result interpretation.

Main Results:

The BPP platform effectively predicts potential participants and products in biochemical reactions.
Extensive experiments demonstrated the effectiveness of the implemented models on a curated dataset.
Case studies confirmed the platform's utility, particularly its chronological pattern analysis.

Conclusions:

The BPP platform provides an efficient and automated solution for biochemical pathway analysis.
Representation learning, especially hypergraph neural networks, shows great potential in modeling biochemical networks.
The freely accessible platform, code, and datasets facilitate further research in the field.