{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T10:11:58Z","timestamp":1767175918433,"version":"build-2238731810"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1012656","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2024,12,27]],"date-time":"2024-12-27T00:00:00Z","timestamp":1735257600000}}],"reference-count":22,"publisher":"Public Library of Science (PLoS)","issue":"12","license":[{"start":{"date-parts":[[2024,12,13]],"date-time":"2024-12-13T00:00:00Z","timestamp":1734048000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union\u2019s Horizon 2020","award":["847949"],"award-info":[{"award-number":["847949"]}]},{"name":"European Union's Horizon Europe program","award":["101070950 (X-PAND)"],"award-info":[{"award-number":["101070950 (X-PAND)"]}]},{"name":"Ram\u00f3n y Cajal","award":["RYC2021-032197-I"],"award-info":[{"award-number":["RYC2021-032197-I"]}]},{"name":"Foundation pour la Recherche M\u00e9dicale","award":["FRM EQU202303016287"],"award-info":[{"award-number":["FRM EQU202303016287"]}]},{"DOI":"10.13039\/501100001677","name":"Institut National de la Sant\u00e9 et de la Recherche M\u00e9dicale","doi-asserted-by":"crossref","award":["ATIP AVENIR"],"award-info":[{"award-number":["ATIP AVENIR"]}],"id":[{"id":"10.13039\/501100001677","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Agence Nationale pour la Recherche","award":["ANR-18-CE14-0006-01, RHU QUID-NASH, ANR-18-IDEX-0001, ANR-22-CE14-0002"],"award-info":[{"award-number":["ANR-18-CE14-0006-01, RHU QUID-NASH, ANR-18-IDEX-0001, ANR-22-CE14-0002"]}]},{"name":"European Union\u2019s Horizon 2020 research and innovation programme","award":["847949"],"award-info":[{"award-number":["847949"]}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>\n                    In the era of precision medicine, it is necessary to understand heterogeneity among patients with complex diseases to improve personalized prevention and management strategies. Here, we introduce\n                    <jats:monospace specific-use=\"no-wrap\">ClustAll<\/jats:monospace>\n                    , a Bioconductor package designed for unsupervised patient stratification using clinical data.\n                    <jats:monospace specific-use=\"no-wrap\">ClustAll<\/jats:monospace>\n                    is based on the previously validated methodology\n                    <jats:monospace specific-use=\"no-wrap\">ClustAll<\/jats:monospace>\n                    , a clustering framework that effectively handles intricacies in clinical data, including mixed data types, missing values, and collinearity. Additionally,\n                    <jats:monospace specific-use=\"no-wrap\">ClustAll<\/jats:monospace>\n                    stands out in its ability to identify multiple patient stratifications within the same population while ensuring their robustness. The updated implementation of\n                    <jats:monospace specific-use=\"no-wrap\">ClustAll<\/jats:monospace>\n                    features S4 classes, parallel computing for enhanced computational efficiency, and user-friendly tools for exploring and comparing stratifications against clinical phenotypes. The performance of\n                    <jats:monospace specific-use=\"no-wrap\">ClustAll<\/jats:monospace>\n                    has been validated using two public clinical datasets, confirming its effectiveness in patient stratification and highlighting its potential impact on clinical management. In summary,\n                    <jats:monospace specific-use=\"no-wrap\">ClustAll<\/jats:monospace>\n                    is a powerful tool for patient stratification in personalized medicine.\n                  <\/jats:p>","DOI":"10.1371\/journal.pcbi.1012656","type":"journal-article","created":{"date-parts":[[2024,12,13]],"date-time":"2024-12-13T13:41:06Z","timestamp":1734097266000},"page":"e1012656","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["ClustAll: An R package for patient stratification in complex diseases"],"prefix":"10.1371","volume":"20","author":[{"given":"Asier","family":"Ortega-Legarreta","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0451-3749","authenticated-orcid":true,"given":"Sara","family":"Palomino-Echeverria","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Estefania","family":"Huergo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vincenzo","family":"Lagani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Narsis A.","family":"Kiani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pierre-Emmanuel","family":"Rautou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nuria Planell","family":"Picola","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jesper","family":"Tegner","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David","family":"Gomez-Cabrero","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2024,12,13]]},"reference":[{"key":"pcbi.1012656.ref001","author":"H Wang","journal-title":"Phenotype clustering in health care: A narrative review for clinicians"},{"key":"pcbi.1012656.ref002","article-title":"Methods for Stratification and Validation Cohorts: A Scoping Review","volume":"12","author":"TT Moral","year":"2022","journal-title":"Journal of Personalized Medicine. 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