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pmid:41133270
E-ABIN: an explainable module for anomaly detection in biological networks.
Ugo Lomoio · Tommaso Mazza · Pierangelo Veltri · Pietro Hiram Guzzi
NAR genomics and bioinformatics · 2025 doi:10.1093/nargab/lqaf135
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Experiment
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Complete file
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Recent advances in artificial intelligence have enabled the identification of aberrant molecular patterns distinguishing disease states from healthy controls. Coupled with improvements in model interpretability, these tools now support the identification of genes potentially driving disease phenotypes. However, current approaches to gene anomaly detection often remain limited to single datasets and lack accessible graphical interfaces. We introduce E-ABIN, a general-purpose, explainable framework for anomaly detection in biological networks. E-ABIN combines classical machine learning and graph-based deep learning techniques within a unified, user-friendly platform, enabling the detection and interpretation of anomalies from gene expression or methylation-derived networks. By integrating algorithms such as support vector machines, random forests, graph autoencoders, and graph adversarial attributed networks, E-ABIN ensures high predictive accuracy while maintaining interpretability. 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