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PathExNET: A tool for extracting pathway expression networks from gene expression statistics.

George Minadakis^1,2, Alfonso Muñoz-Pomer Fuentes³, George Tsouloupas⁴

¹Bioinformatics Department, The Cyprus Institute of Neurology & Genetics, 6 Iroon Avenue, 2371 Ayios Dometios, Nicosia, Cyprus.

Computational and Structural Biotechnology Journal

|August 25, 2021

Summary

This study introduces PathExNET, a tool for creating pathway networks to identify unique molecular signatures for complex diseases. This approach aids in disease individualization and personalized medicine by analyzing gene expression patterns.

Keywords:

Differential expression analysis Pathway analysis Pathway expression networks

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Area of Science:

Bioinformatics
Systems Biology
Computational Biology

Background:

Understanding molecular mechanisms in complex diseases requires analyzing how biological pathways function across experiments.
Current methods lack a standardized approach to characterize pathway behavior in disease contexts.

Purpose of the Study:

To develop a network-based method for numerically characterizing biological pathways across different experiments.
To create unique pathway network 'footprints' for disease characterization.
To facilitate personalized medicine through improved disease understanding.

Main Methods:

Utilized network-based approaches to analyze gene expression data.
Developed PathExNET, a web service for creating pathway-to-pathway expression networks.
Incorporated over- and under-expression information from differential gene expression analyses.

Main Results:

PathExNET enables the creation of pathway networks with quantitative expression status.
Generated unique numeric characterizations of pathway expression for specific diseases.
Facilitated the generation of diverse pathway network combinations.

Conclusions:

The numeric characterization of pathway expression status offers a novel approach to disease individualization.
PathExNET contributes to a more precise understanding of diseases at the pathway network level.
This methodology supports the advancement of personalized medicine and treatment management.