{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T18:29:46Z","timestamp":1776709786708,"version":"3.51.2"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1012022","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2024,4,24]],"date-time":"2024-04-24T00:00:00Z","timestamp":1713916800000}}],"reference-count":81,"publisher":"Public Library of Science (PLoS)","issue":"4","license":[{"start":{"date-parts":[[2024,4,12]],"date-time":"2024-04-12T00:00:00Z","timestamp":1712880000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100014438","name":"Business Finland","doi-asserted-by":"crossref","award":["6478\/31\/2019"],"award-info":[{"award-number":["6478\/31\/2019"]}],"id":[{"id":"10.13039\/501100014438","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100002341","name":"Academy of Finland","doi-asserted-by":"crossref","award":["339763"],"award-info":[{"award-number":["339763"]}],"id":[{"id":"10.13039\/501100002341","id-type":"DOI","asserted-by":"crossref"}]},{"name":"European Union\u2019s Horizon 2020 research and innovation","award":["101034307"],"award-info":[{"award-number":["101034307"]}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>The Patient Similarity Network paradigm implies modeling the similarity between patients based on specific data. The similarity can summarize patients\u2019 relationships from high-dimensional data, such as biological omics. The end PSN can undergo un\/supervised learning tasks while being strongly interpretable, tailored for precision medicine, and ready to be analyzed with graph-theory methods. However, these benefits are not guaranteed and depend on the granularity of the summarized data, the clarity of the similarity measure, the complexity of the network\u2019s topology, and the implemented methods for analysis. To date, no patient classifier fully leverages the paradigm\u2019s inherent benefits. PSNs remain complex, unexploited, and meaningless. We present StellarPath, a hierarchical-vertical patient classifier that leverages pathway analysis and patient similarity concepts to find meaningful features for both classes and individuals. StellarPath processes omics data, hierarchically integrates them into pathways, and uses a novel similarity to measure how patients\u2019 pathway activity is alike. It selects biologically relevant molecules, pathways, and networks, considering molecule stability and topology. A graph convolutional neural network then predicts unknown patients based on known cases. StellarPath excels in classification performances and computational resources across sixteen datasets. It demonstrates proficiency in inferring the class of new patients described in external independent studies, following its initial training and testing phases on a local dataset. It advances the PSN paradigm and provides new markers, insights, and tools for in-depth patient profiling.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1012022","type":"journal-article","created":{"date-parts":[[2024,4,12]],"date-time":"2024-04-12T17:21:33Z","timestamp":1712942493000},"page":"e1012022","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":6,"title":["StellarPath: Hierarchical-vertical multi-omics classifier synergizes stable markers and interpretable similarity networks for patient 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