Version 1
: Received: 19 September 2024 / Approved: 19 September 2024 / Online: 23 September 2024 (13:39:28 CEST)
How to cite:
Guzzi, P. H.; Lomoio, U.; Mazza, T.; Veltri, P. Anomaly Detection in Individual Specific Networks through Explainable Generative Adversarial Attributed Networks. Preprints2024, 2024091565. https://doi.org/10.20944/preprints202409.1565.v1
Guzzi, P. H.; Lomoio, U.; Mazza, T.; Veltri, P. Anomaly Detection in Individual Specific Networks through Explainable Generative Adversarial Attributed Networks. Preprints 2024, 2024091565. https://doi.org/10.20944/preprints202409.1565.v1
Guzzi, P. H.; Lomoio, U.; Mazza, T.; Veltri, P. Anomaly Detection in Individual Specific Networks through Explainable Generative Adversarial Attributed Networks. Preprints2024, 2024091565. https://doi.org/10.20944/preprints202409.1565.v1
APA Style
Guzzi, P. H., Lomoio, U., Mazza, T., & Veltri, P. (2024). Anomaly Detection in Individual Specific Networks through Explainable Generative Adversarial Attributed Networks. Preprints. https://doi.org/10.20944/preprints202409.1565.v1
Chicago/Turabian Style
Guzzi, P. H., Tommaso Mazza and Pierangelo Veltri. 2024 "Anomaly Detection in Individual Specific Networks through Explainable Generative Adversarial Attributed Networks" Preprints. https://doi.org/10.20944/preprints202409.1565.v1
Abstract
Recently, the availability of many omics data source has given the rise of modelling biological networks for each individual or patient. Such networks are able to represent individual-specific characteristics, providing insights into the condition of each person. Given a set of networks of individuals, a network representing a particular condition (e.g., an individual with a specific disease) may be seen as an anomaly network. Consequently, the use of Graph Anomaly Detection techniques may support such analysis. Among the others, Generative Adversarial Networks present optimal per- formances in anomaly detection. This paper presents ADIN (Anomaly Detection in Individual Networks), a framework based on Generative Adversarial Attributed Networks (GAANs) for anomaly detection in convergence/divergence patients at- tributed networks. Preliminary results on networks generated from computational biology gene expression data demonstrate the effectiveness of our approach in detecting and explaining bladder cancer patients.
Keywords
ISN; GANN; Patient
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
