{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T17:59:23Z","timestamp":1760551163768,"version":"3.37.3"},"reference-count":48,"publisher":"Oxford University Press (OUP)","issue":"20","license":[{"start":{"date-parts":[[2020,7,8]],"date-time":"2020-07-08T00:00:00Z","timestamp":1594166400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100003074","name":"Agencia Nacional de Promoci\u00f3n Cient\u00edfica y Tecnol\u00f3gica PICT","doi-asserted-by":"crossref","award":["2015-2607","PICT 2015-1435"],"award-info":[{"award-number":["2015-2607","PICT 2015-1435"]}],"id":[{"id":"10.13039\/501100003074","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Universidad Nacional de Cuyo SECTyP J078","award":["J062","J096"],"award-info":[{"award-number":["J062","J096"]}]},{"DOI":"10.13039\/501100002923","name":"Consejo Nacional de Investigaciones Cient\u00edficas y T\u00e9cnicas","doi-asserted-by":"crossref","award":["PUE 22920160100074CO."],"award-info":[{"award-number":["PUE 22920160100074CO."]}],"id":[{"id":"10.13039\/501100002923","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,12,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Statistical and machine-learning analyses of tumor transcriptomic profiles offer a powerful resource to gain deeper understanding of tumor subtypes and disease prognosis. Currently, prognostic gene-expression signatures do not exist for all cancer types, and most developed to date have been optimized for individual tumor types. In Galgo, we implement a bi-objective optimization approach that prioritizes gene signature cohesiveness and patient survival in parallel, which provides greater power to identify tumor transcriptomic phenotypes strongly associated with patient survival.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>To compare the predictive power of the signatures obtained by Galgo with previously studied subtyping methods, we used a meta-analytic approach testing a total of 35 large population-based transcriptomic biobanks of four different cancer types. Galgo-generated colorectal and lung adenocarcinoma signatures were stronger predictors of patient survival compared to published molecular classification schemes. One Galgo-generated breast cancer signature outperformed PAM50, AIMS, SCMGENE and IntClust subtyping predictors. In high-grade serous ovarian cancer, Galgo signatures obtained similar predictive power to a consensus classification method. In all cases, Galgo subtypes reflected enrichment of gene sets related to the hallmarks of the disease, which highlights the biological relevance of the partitions found.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The open-source R package is available on www.github.com\/harpomaxx\/galgo.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaa619","type":"journal-article","created":{"date-parts":[[2020,7,1]],"date-time":"2020-07-01T03:20:23Z","timestamp":1593573623000},"page":"5037-5044","source":"Crossref","is-referenced-by-count":4,"title":["Galgo: a bi-objective evolutionary meta-heuristic identifies robust transcriptomic classifiers associated with patient outcome across multiple cancer types"],"prefix":"10.1093","volume":"36","author":[{"given":"M E","family":"Guerrero-Gimenez","sequence":"first","affiliation":[{"name":"Laboratory of Oncology, Institute of Medicine and Experimental Biology of Cuyo (IMBECU), National Scientific and Technical Research Council (CONICET) , Mendoza 5500, Argentina"},{"name":"Institute of Biochemistry and Biotechnology, School of Medicine, National University of Cuyo , Mendoza 5500, Argentina"}]},{"given":"J M","family":"Fernandez-Mu\u00f1oz","sequence":"additional","affiliation":[{"name":"Laboratory of Oncology, Institute of Medicine and Experimental Biology of Cuyo (IMBECU), National Scientific and Technical Research Council (CONICET) , Mendoza 5500, Argentina"},{"name":"Institute of Biochemistry and Biotechnology, School of Medicine, National University of Cuyo , Mendoza 5500, Argentina"}]},{"given":"B J","family":"Lang","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Beth Israel Deaconess Medical Center, Harvard Medical School , Boston, MA 02215, USA"}]},{"given":"K M","family":"Holton","sequence":"additional","affiliation":[{"name":"Harvard Department of Stem Cell and Regenerative Biology , Cambridge, MA 02138, USA"}]},{"given":"D R","family":"Ciocca","sequence":"additional","affiliation":[{"name":"Laboratory of Oncology, Institute of Medicine and Experimental Biology of Cuyo (IMBECU), National Scientific and Technical Research Council (CONICET) , Mendoza 5500, Argentina"}]},{"given":"C A","family":"Catania","sequence":"additional","affiliation":[{"name":"Laboratory of Intelligent Systems (LABSIN), Engineering School, National University of Cuyo , Mendoza 5500, Argentina"}]},{"given":"F C M","family":"Zoppino","sequence":"additional","affiliation":[{"name":"Laboratory of Oncology, Institute of Medicine and Experimental Biology of Cuyo (IMBECU), National Scientific and Technical Research Council (CONICET) , Mendoza 5500, Argentina"}]}],"member":"286","published-online":{"date-parts":[[2020,7,8]]},"reference":[{"key":"2023062408113476700_btaa619-B1","doi-asserted-by":"crossref","first-page":"e108","DOI":"10.1371\/journal.pbio.0020108","article-title":"Semi-supervised methods to predict patient survival from gene expression data","volume":"2","author":"Bair","year":"2004","journal-title":"PLoS Biol"},{"key":"2023062408113476700_btaa619-B2","doi-asserted-by":"crossref","first-page":"e30269","DOI":"10.1371\/journal.pone.0030269","article-title":"Angiogenic mRNA and microRNA gene expression signature predicts a novel subtype of serous ovarian cancer","volume":"7","author":"Bentink","year":"2012","journal-title":"PLoS One"},{"key":"2023062408113476700_btaa619-B3","doi-asserted-by":"crossref","first-page":"5037","DOI":"10.1158\/1078-0432.CCR-18-0784","article-title":"Consensus on molecular subtypes of high-grade serous ovarian carcinoma","volume":"24","author":"Chen","year":"2018","journal-title":"Clin. 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