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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

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PyGenePlexus: a Python package for gene discovery using network-based machine learning.

Christopher A Mancuso^1,2, Renming Liu¹, Arjun Krishnan^1,3

¹Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI 48824, USA.

Bioinformatics (Oxford, England)

|February 1, 2023

Summary

This summary is machine-generated.

PyGenePlexus is a Python package that uses machine learning and molecular interaction networks to predict gene associations. It offers insights into gene sets by comparing models and revealing network connectivity for enhanced biological discovery.

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Area of Science:

Bioinformatics
Computational Biology
Systems Biology

Background:

Understanding gene set associations is crucial for biological discovery.
Existing methods may lack interpretability and network context.

Purpose of the Study:

To introduce PyGenePlexus, a Python package for analyzing gene sets.
To provide a machine learning-based approach for predicting gene associations within molecular interaction networks.

Main Methods:

Utilizes a supervised machine learning model informed by molecular interaction networks.
Compares models trained on user-defined gene sets against a large database of known gene sets.
Predicts gene associations and returns network connectivity of top-ranked genes.

Main Results:

PyGenePlexus predicts gene associations within a network context.
The package offers interpretability by comparing models.
Identifies network connectivity for top predicted genes.

Conclusions:

PyGenePlexus provides a novel computational tool for gene set analysis.
The package enhances biological insight through network-informed machine learning.
Facilitates the discovery of gene relationships and network structures.