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Anomaly Detection in Individual Specific Networks through Explainable Generative Adversarial Attributed Networks
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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.
Title: Anomaly Detection in Individual Specific Networks through Explainable Generative Adversarial Attributed Networks
Description:
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.
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