Network-based anomaly detection algorithm reveals proteins with major roles in human tissues

Background: Anomaly detection in graphs is critical in various domains, notably in medicine and biology, where anomalies often encapsulate pivotal information. Here, we focused on network analysis of molecular interactions between proteins, which is commonly used to study and infer the impact of proteins on health and disease. In such a network, an anomalous protein might indicate its impact on the organism's health. Results: We propose Weighted Graph Anomalous Node Detection (WGAND), a novel machine learning-based method for detecting anomalies in weighted graphs. WGAND is based on the observation that edge patterns of anomalous nodes tend to deviate significantly from expected patterns. We quantified these deviations to generate features, and utilized the resulting features to model the anomaly of nodes, resulting in node anomaly scores. We created four variants of the WGAND methods and compared them to two previously-published (baseline) methods. We evaluated WGAND on data of protein interactions in 17 human tissues, where anomalous nodes corresponded to proteins with major roles in tissue contexts. In 13 of the tissues, WGAND obtained higher AUC and P@K than baseline methods. We demonstrate that WGAND effectively identified proteins that participate in tissue-specific processes and diseases. Conclusion: We present WGAND, a new approach to anomaly detection in weighted graphs. Our results underscore its capability to highlight critical proteins within protein-protein interaction networks. WGAND holds the promise to enhance our understanding of intricate biological processes and might pave the way for novel therapeutic strategies targeting tissue-specific diseases. Its versatility ensures its applicability across diverse weighted graphs, making it a robust tool for detecting anomalous nodes. ### Competing Interest Statement The authors have declared no competing interest.