biostudies-literatureUnknown14Kagan DBen-Gurion University of the NegevIsrael Science Foundation<h4>Background</h4>Proteins act through physical interactions with other molecules to maintain organismal health. Protein-protein interaction (PPI) networks have proved to be a powerful framework for obtaining insight into protein functions, cellular organization, response to signals, and disease states. In multicellular organisms, protein content varies between tissues, influencing tissue morphology and function. Weighted PPI networks, reflecting the likelihood of interactions in specific tissues, offer insights into tissue-specific processes and disease mechanisms. We hypothesized that detecting anomalous nodes in these networks could reveal proteins with key tissue-specific functions.<h4>Results</h4>Here, we introduce Weighted Graph Anomalous Node Detection (WGAND), a novel machine-learGigaSciencegiaf034https://www.ebi.ac.uk/biostudies/studies/S-EPMC11976396biostudies-literatureNetwork-based anomaly detection algorithm reveals proteins with major roles in human tissues.PMC11976396Kagan DFire MYeger-Lotem EJubran JfalseNetwork-based anomaly detection algorithm reveals proteins with major roles in human tissues.<h4>Background</h4>Proteins act through physical interactions with other molecules to maintain organismal health. Protein-protein interaction (PPI) networks have proved to be a powerful framework for obtaining insight into protein functions, cellular organization, response to signals, and disease states. In multicellular organisms, protein content varies between tissues, influencing tissue morphology and function. Weighted PPI networks, reflecting the likelihood of interactions in specific tissues, offer insights into tissue-specific processes and disease mechanisms. We hypothesized that detecting anomalous nodes in these networks could reveal proteins with key tissue-specific functions.<h4>Results</h4>Here, we introduce Weighted Graph Anomalous Node Detection (WGAND), a novel machine-lear2025-01-01T00:00:00Z2025 Jan2026-06-01T23:05:04.621Z2025-07-05T03:04:52.905ZS-EPMC119763964019782210.1093/gigascience/giaf034