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E-ABIN: an explainable module for anomaly detection in biological networks.

Ugo Lomoio^1,2, Tommaso Mazza³, Pierangelo Veltri⁴

¹Department of Surgical and Medical Sciences, Magna Graecia University, Viale Europa, 88100 Catanzaro, Italy.

NAR Genomics and Bioinformatics

|October 24, 2025

Summary

This summary is machine-generated.

E-ABIN is a new explainable framework for detecting anomalies in biological networks using gene expression data. This tool helps identify disease-driving genes with high accuracy and interpretability.

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Area of Science:

Bioinformatics
Computational Biology
Artificial Intelligence in Genomics

Background:

Large-scale omics data require advanced analytical tools for gene expression analysis.
Artificial intelligence (AI) aids in identifying molecular patterns distinguishing diseases.
Current gene anomaly detection methods are often dataset-specific and lack user-friendly interfaces.

Purpose of the Study:

To introduce E-ABIN, a versatile and explainable framework for anomaly detection in biological networks.
To provide a unified platform integrating classical machine learning and graph-based deep learning for biological data analysis.
To enable the detection and interpretation of anomalies from gene expression or methylation-derived networks.

Main Methods:

Integration of Support Vector Machines (SVM), Random Forests, Graph Autoencoders, and Graph Adversarial Attributed Networks (GAAN).
Development of a user-friendly platform for anomaly detection and interpretation in biological networks.
Application of the framework to gene expression and methylation data.

Main Results:

E-ABIN demonstrates high predictive accuracy and maintains model interpretability.
Successful identification of biologically relevant anomalies in case studies of bladder cancer and celiac disease.
The framework provides valuable insights into disease mechanisms by uncovering gene anomalies.

Conclusions:

E-ABIN offers a robust and explainable solution for anomaly detection in complex biological networks.
The framework enhances the understanding of disease phenotypes by identifying potential driver genes.
E-ABIN is freely available, promoting accessibility for researchers in bioinformatics and computational biology.