{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T18:16:14Z","timestamp":1787940974228,"version":"build-2784847793"},"reference-count":64,"publisher":"Oxford University Press (OUP)","issue":"8","license":[{"start":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T00:00:00Z","timestamp":1784851200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,8,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Molecular scoring is a popular approach for studying pathway-level functional alterations with omics data. Using molecular scores for tasks such as single-sample molecular characterisation, phenotype prediction or disease stratification has several advantages compared to using omics data directly. Molecular scores provide biological interpretability and are more generalisable across datasets, facilitating data integration and machine learning applications. However, numerous scoring methods are available through different software packages, and currently there is a lack of tools to easily use these scores for model training and prediction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We developed pathMED, an R\/Bioconductor package that unifies various scoring methods in a simple framework. Furthermore, pathMED also contains a machine learning module to train and test models that use the calculated molecular scores to predict clinical outcomes. We demonstrate some of its potential applications in three use cases using public omics data. We showed the generalisability of machine learning models trained on transcriptomic scores in predicting clinical outcomes when deploying on proteomic scores. We also demonstrated the application of transcriptomics scores in predicting breast cancer treatment response and identifying pathways strongly associated to tumour biology and treatment response. Finally, we demonstrated the benefit of integrating a novel gene set dissection step into the analysis pipeline to resolve disease heterogeneity at the pathway level.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability<\/jats:title>\n                    <jats:p>PathMED is freely available in the Bioconductor repository (https:\/\/bioconductor.org\/packages\/release\/bioc\/html\/pathMED.html). Code to reproduce the analyses is publicly available at https:\/\/github.com\/GENyO-BioInformatics\/pathMED_article.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag519","type":"journal-article","created":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T11:51:50Z","timestamp":1784807510000},"source":"Crossref","is-referenced-by-count":0,"title":["PathMED: an R toolkit for single-sample molecular scoring and machine learning with omics data"],"prefix":"10.1093","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5186-0735","authenticated-orcid":false,"given":"Jordi","family":"Martorell-Marug\u00e1n","sequence":"first","affiliation":[{"name":"Computational Neuroscience, Joint Unit in Biomedical Imaging and Artificial Intelligence FISABIO\u2013CIPF, Foundation for the Promotion of Health and Biomedical Research in the Valencian Region (FISABIO) , Valencia 46012,","place":["Spain"]},{"name":"Bioinformatics and Health Data Science, GENYO, Centre for Genomics and Oncological Research: Pfizer, University of Granada, Andalusian Regional Government, PTS Granada , Granada 18016,","place":["Spain"]},{"name":"Fundaci\u00f3n para la Investigaci\u00f3n Biosanitaria de Andaluc\u00eda Oriental-Alejandro Otero (FIBAO) , Granada 18012,","place":["Spain"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ivan","family":"Ellson","sequence":"additional","affiliation":[{"name":"Bioinformatics and Health Data Science, GENYO, Centre for Genomics and Oncological Research: Pfizer, University of Granada, Andalusian Regional Government, PTS Granada , Granada 18016,","place":["Spain"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8634-117X","authenticated-orcid":false,"given":"Ra\u00fal","family":"L\u00f3pez-Dom\u00ednguez","sequence":"additional","affiliation":[{"name":"Bioinformatics and Health Data Science, GENYO, Centre for Genomics and Oncological Research: Pfizer, University of Granada, Andalusian Regional Government, PTS Granada , Granada 18016,","place":["Spain"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pablo Pedro","family":"Jurado-Basc\u00f3n","sequence":"additional","affiliation":[{"name":"Bioinformatics and Health Data Science, GENYO, Centre for Genomics and Oncological Research: Pfizer, University of Granada, Andalusian Regional Government, PTS Granada , Granada 18016,","place":["Spain"]},{"name":"Department of Statistics and Operational Research, University of Granada , Granada 18071,","place":["Spain"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan Antonio","family":"Villatoro-Garc\u00eda","sequence":"additional","affiliation":[{"name":"Bioinformatics and Health Data Science, GENYO, Centre for Genomics and Oncological Research: Pfizer, University of Granada, Andalusian Regional Government, PTS Granada , Granada 18016,","place":["Spain"]},{"name":"Department of Statistics and Operational Research, University of Granada , Granada 18071,","place":["Spain"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chang","family":"Wang","sequence":"additional","affiliation":[{"name":"Translational Early Program Lead, ICV Translational Early Development, Bristol Myers Squibb , Lawrence, NJ 08543,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fr\u00e9d\u00e9ric","family":"Baribaud","sequence":"additional","affiliation":[{"name":"Translational Early Program Lead, ICV Translational Early Development, Bristol Myers Squibb , Lawrence, NJ 08543,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8440-312X","authenticated-orcid":false,"given":"Daniel","family":"Toro-Dom\u00ednguez","sequence":"additional","affiliation":[{"name":"Department of Environmental Medicine, Karolinska Institutet, Unit of Inflammatory Diseases , Solna,","place":["Sweden"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pedro","family":"Carmona-S\u00e1ez","sequence":"additional","affiliation":[{"name":"Bioinformatics and Health Data Science, GENYO, Centre for Genomics and Oncological Research: Pfizer, University of Granada, Andalusian Regional Government, PTS Granada , Granada 18016,","place":["Spain"]},{"name":"Department of Statistics and Operational Research, University of Granada , Granada 18071,","place":["Spain"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,7,24]]},"reference":[{"key":"2026082813170476200_btag519-B1","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1186\/1471-2105-10-47","article-title":"A general modular framework for gene set enrichment analysis","volume":"10","author":"Ackermann","year":"2009","journal-title":"BMC Bioinformatics"},{"key":"2026082813170476200_btag519-B2","doi-asserted-by":"publisher","first-page":"D679","DOI":"10.1093\/nar\/gkad960","article-title":"WikiPathways 2024: next generation pathway database","volume":"52","author":"Agrawal","year":"2024","journal-title":"Nucleic Acids Res"},{"key":"2026082813170476200_btag519-B3","doi-asserted-by":"publisher","first-page":"1083","DOI":"10.1038\/nmeth.4463","article-title":"SCENIC: single-cell regulatory network inference and clustering","volume":"14","author":"Aibar","year":"2017","journal-title":"Nat Methods"},{"key":"2026082813170476200_btag519-B4","doi-asserted-by":"publisher","first-page":"iyad031","DOI":"10.1093\/genetics\/iyad031","article-title":"The gene ontology knowledgebase in 2023","volume":"224","author":"Aleksander","year":"2023","journal-title":"Genetics"},{"key":"2026082813170476200_btag519-B5","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1038\/75556","article-title":"Gene ontology: tool for the unification of biology","volume":"25","author":"Ashburner","year":"2000","journal-title":"Nat Genet"},{"key":"2026082813170476200_btag519-B6","doi-asserted-by":"publisher","first-page":"vbac016","DOI":"10.1093\/bioadv\/vbac016","article-title":"decoupleR: ensemble of computational methods to infer biological activities from omics data","volume":"2","author":"Badia-I-Mompel","year":"2022","journal-title":"Bioinform Adv"},{"key":"2026082813170476200_btag519-B7","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1038\/nature08460","article-title":"Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1","volume":"462","author":"Barbie","year":"2009","journal-title":"Nature"},{"key":"2026082813170476200_btag519-B8","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach Learn"},{"key":"2026082813170476200_btag519-B9","doi-asserted-by":"publisher","DOI":"10.1201\/9781315139470","volume-title":"Classification and Regression Trees","author":"Breiman","year":"1984"},{"key":"2026082813170476200_btag519-B10","doi-asserted-by":"publisher","first-page":"e1257452","DOI":"10.1080\/2162402X.2016.1257452","article-title":"Tumor-infiltrating lymphocyte composition, organization and PD-1\/ PD-L1 expression are linked in breast cancer","volume":"6","author":"Buisseret","year":"2017","journal-title":"Oncoimmunology"},{"key":"2026082813170476200_btag519-B11","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1016\/j.immuni.2008.05.012","article-title":"A modular analysis framework for blood genomics studies: application to systemic lupus erythematosus","volume":"29","author":"Chaussabel","year":"2008","journal-title":"Immunity"},{"key":"2026082813170476200_btag519-B12","doi-asserted-by":"publisher","author":"Chen","DOI":"10.1145\/2939672.2939785"},{"key":"2026082813170476200_btag519-B13","doi-asserted-by":"publisher","first-page":"gkaf018","DOI":"10.1093\/nar\/gkaf018","article-title":"edgeR v4: powerful differential analysis of sequencing data with expanded functionality and improved support for small counts and larger datasets","volume":"53","author":"Chen","year":"2025","journal-title":"Nucleic Acids Res"},{"key":"2026082813170476200_btag519-B14","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1016\/j.cell.2020.06.012","article-title":"Proteogenomics of non-smoking lung cancer in east asia delineates molecular signatures of pathogenesis and progression","volume":"182","author":"Chen","year":"2020","journal-title":"Cell"},{"key":"2026082813170476200_btag519-B15","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1186\/s12864-019-6413-7","article-title":"The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation","volume":"21","author":"Chicco","year":"2020","journal-title":"BMC Genomics"},{"key":"2026082813170476200_btag519-B16","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1186\/s13040-023-00322-4","article-title":"The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification","volume":"16","author":"Chicco","year":"2023","journal-title":"BioData Min"},{"key":"2026082813170476200_btag519-B17","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach Learn"},{"key":"2026082813170476200_btag519-B18","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1111\/j.2517-6161.1958.tb00292.x","article-title":"The regression analysis of binary sequences","volume":"20","author":"Cox","year":"1958","journal-title":"J R Stat Soc Ser B Methodol"},{"key":"2026082813170476200_btag519-B19","doi-asserted-by":"publisher","first-page":"1930","DOI":"10.1109\/ICNN.1997.614194","article-title":"Gauss-Newton approximation to Bayesian learning","volume":"3","author":"Dan Foresee","year":"1997","journal-title":"Proc Int Conf Neural Netw ICNN97"},{"key":"2026082813170476200_btag519-B20","doi-asserted-by":"publisher","first-page":"D1328","DOI":"10.1093\/nar\/gkae883","article-title":"Comparative Toxicogenomics Database\u2019s 20th anniversary: update 2025","volume":"53","author":"Davis","year":"2025","journal-title":"Nucleic Acids Res"},{"key":"2026082813170476200_btag519-B21","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1200\/JCO.2009.23.7370","article-title":"Tumor-associated lymphocytes as an independent predictor of response to neoadjuvant chemotherapy in breast cancer","volume":"28","author":"Denkert","year":"2010","journal-title":"J Clin Oncol"},{"key":"2026082813170476200_btag519-B22","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1214\/009053604000000067","article-title":"Least angle regression","volume":"32","author":"Efron","year":"2004","journal-title":"Ann Statist"},{"key":"2026082813170476200_btag519-B23","doi-asserted-by":"publisher","first-page":"566","DOI":"10.1091\/mbc.E18-08-0531","article-title":"Role of JAM-A tyrosine phosphorylation in epithelial barrier dysfunction during intestinal inflammation","volume":"30","author":"Fan","year":"2019","journal-title":"Mol Biol Cell"},{"key":"2026082813170476200_btag519-B24","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1186\/s12859-018-2435-4","article-title":"Single sample scoring of molecular phenotypes","volume":"19","author":"Foroutan","year":"2018","journal-title":"BMC Bioinformatics"},{"key":"2026082813170476200_btag519-B25","doi-asserted-by":"publisher","DOI":"10.3390\/biomedicines10030590","article-title":"Functional enrichment analysis of regulatory elements","volume":"10","author":"Garcia-Moreno","year":"2022","journal-title":"Biomedicines"},{"key":"2026082813170476200_btag519-B26","doi-asserted-by":"publisher","first-page":"D1333","DOI":"10.1093\/nar\/gkad1005","article-title":"The Human Phenotype Ontology in 2024: phenotypes around the world","volume":"52","author":"Gargano","year":"2024","journal-title":"Nucleic Acids Res"},{"key":"2026082813170476200_btag519-B27","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1186\/1471-2105-14-7","article-title":"GSVA: gene set variation analysis for microarray and RNA-Seq data","volume":"14","author":"H\u00e4nzelmann","year":"2013","journal-title":"BMC Bioinformatics"},{"key":"2026082813170476200_btag519-B28","doi-asserted-by":"publisher","first-page":"e1004310","DOI":"10.1371\/journal.pcbi.1004310","article-title":"Time-course gene set analysis for longitudinal gene expression data","volume":"11","author":"Hejblum","year":"2015","journal-title":"PLoS Comput Biol"},{"key":"2026082813170476200_btag519-B29","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1186\/s13073-023-01237-9","article-title":"Analysis of transcriptomic features reveals molecular endotypes of SLE with clinical implications","volume":"15","author":"Hubbard","year":"2023","journal-title":"Genome Med"},{"key":"2026082813170476200_btag519-B30","doi-asserted-by":"publisher","first-page":"1996","DOI":"10.1200\/JCO.2011.39.5624","article-title":"Gene modules and response to neoadjuvant chemotherapy in breast cancer subtypes: a pooled analysis","volume":"30","author":"Ignatiadis","year":"2012","journal-title":"J Clin Oncol"},{"key":"2026082813170476200_btag519-B31","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1093\/nar\/28.1.27","article-title":"KEGG: Kyoto Encyclopedia of Genes and Genomes","volume":"28","author":"Kanehisa","year":"2000","journal-title":"Nucleic Acids Res"},{"key":"2026082813170476200_btag519-B32","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1038\/nrg.2018.4","article-title":"Integrative omics for health and disease","volume":"19","author":"Karczewski","year":"2018","journal-title":"Nat Rev Genet"},{"key":"2026082813170476200_btag519-B33","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.cels.2017.11.001","article-title":"The library of integrated network-based cellular signatures NIH program: system-level cataloging of human cells response to perturbations","volume":"6","author":"Keenan","year":"2018","journal-title":"Cell Syst"},{"key":"2026082813170476200_btag519-B34","volume-title":"Linear Regression and Correlation","author":"Kenney","year":"1962","edition":"3rd ed"},{"key":"2026082813170476200_btag519-B35","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1089\/jir.2017.0127","article-title":"Development of a validated interferon score using NanoString technology","volume":"38","author":"Kim","year":"2018","journal-title":"J Interferon Cytokine Res"},{"key":"2026082813170476200_btag519-B36","doi-asserted-by":"publisher","author":"Korotkevich","year":"2021","DOI":"10.1101\/060012"},{"key":"2026082813170476200_btag519-B37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v028.i05","article-title":"Building predictive models in R using the caret package, articles","volume":"28","author":"Kuhn","year":"2008","journal-title":"J Stat Soft"},{"key":"2026082813170476200_btag519-B38","doi-asserted-by":"publisher","first-page":"e1000217","DOI":"10.1371\/journal.pcbi.1000217","article-title":"Inferring pathway activity toward precise disease classification","volume":"4","author":"Lee","year":"2008","journal-title":"PLoS Comput Biol"},{"key":"2026082813170476200_btag519-B39","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1038\/ni.2789","article-title":"Molecular signatures of antibody responses derived from a systems biology study of five human vaccines","volume":"15","author":"Li","year":"2014","journal-title":"Nat Immunol"},{"key":"2026082813170476200_btag519-B40","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1162\/neco.1992.4.3.415","article-title":"Bayesian interpolation","volume":"4","author":"MacKay","year":"1992","journal-title":"Neural Comput"},{"key":"2026082813170476200_btag519-B41","doi-asserted-by":"publisher","volume-title":"Generalized Linear Models","author":"McCullagh","DOI":"10.1201\/9780203753736"},{"key":"2026082813170476200_btag519-B42","doi-asserted-by":"publisher","DOI":"10.1172\/jci.insight.97506","article-title":"The phosphatidic acid phosphatase lipin-1 facilitates inflammation-driven Colon carcinogenesis","volume":"3","author":"Meana","year":"2018","journal-title":"JCI Insight"},{"key":"2026082813170476200_btag519-B43","doi-asserted-by":"publisher","first-page":"D672","DOI":"10.1093\/nar\/gkad1025","article-title":"The reactome pathway knowledgebase 2024","volume":"52","author":"Milacic","year":"2024","journal-title":"Nucleic Acids Res"},{"key":"2026082813170476200_btag519-B44","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1007\/978-0-387-88615-2_4","volume-title":"Data Mining in Agriculture","author":"Mucherino","year":"2009"},{"key":"2026082813170476200_btag519-B45","doi-asserted-by":"publisher","first-page":"979","DOI":"10.1038\/modpathol.2008.76","article-title":"Profiling the expression pattern of GPI transamidase complex subunits in human cancer","volume":"21","author":"Nagpal","year":"2008","journal-title":"Mod Pathol"},{"key":"2026082813170476200_btag519-B46","doi-asserted-by":"publisher","first-page":"e1006951","DOI":"10.1371\/journal.pcbi.1006951","article-title":"I_MDS: an inflammatory bowel disease molecular activity score to classify patients with differing disease-driving pathways and therapeutic response to anti-TNF treatment","volume":"15","author":"Pavlidis","year":"2019","journal-title":"PLoS Comput Biol"},{"key":"2026082813170476200_btag519-B47","doi-asserted-by":"publisher","first-page":"D845","DOI":"10.1093\/nar\/gkz1021","article-title":"The DisGeNET knowledge platform for disease genomics: 2019 update","volume":"48","author":"Pi\u00f1ero","year":"2020","journal-title":"Nucleic Acids Res"},{"key":"2026082813170476200_btag519-B48","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511812651","volume-title":"Pattern Recognition and Neural Networks","author":"Ripley","year":"1996"},{"key":"2026082813170476200_btag519-B49","doi-asserted-by":"publisher","first-page":"R25","DOI":"10.1186\/gb-2010-11-3-r25","article-title":"A scaling normalization method for differential expression analysis of RNA-seq data","volume":"11","author":"Robinson","year":"2010","journal-title":"Genome Biol"},{"key":"2026082813170476200_btag519-B50","doi-asserted-by":"publisher","first-page":"623","DOI":"10.1038\/s41586-021-04278-5","article-title":"Multi-omic machine learning predictor of breast cancer therapy response","volume":"601","author":"Sammut","year":"2022","journal-title":"Nature"},{"key":"2026082813170476200_btag519-B51","doi-asserted-by":"publisher","first-page":"1731","DOI":"10.1016\/j.ccell.2025.07.011","article-title":"Integrative analysis of lung adenocarcinoma across diverse ethnicities and exposures","volume":"43","author":"Satpathy","year":"2025","journal-title":"Cancer Cell"},{"key":"2026082813170476200_btag519-B52","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1007\/978-3-642-41136-6_5","volume-title":"Empirical Inference: Festschrift in Honor of Vladimir N. Vapnik","author":"Schapire","year":"2013"},{"key":"2026082813170476200_btag519-B53","doi-asserted-by":"publisher","first-page":"1625","DOI":"10.1056\/NEJM198712243172603","article-title":"Coated oral 5-aminosalicylic acid therapy for mildly to moderately active ulcerative colitis","volume":"317","author":"Schroeder","year":"1987","journal-title":"N Engl J Med"},{"key":"2026082813170476200_btag519-B54","doi-asserted-by":"publisher","first-page":"2046","DOI":"10.3390\/ijms18102046","article-title":"Characterization of growth hormone resistance in experimental and ulcerative colitis","volume":"18","author":"Soendergaard","year":"2017","journal-title":"Int J Mol Sci"},{"key":"2026082813170476200_btag519-B55","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1038\/s41525-020-00151-y","article-title":"Improved detection of tumor suppressor events in single-cell RNA-Seq data","volume":"5","author":"Teschendorff","year":"2020","journal-title":"Npj Genomic Med"},{"key":"2026082813170476200_btag519-B56","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1186\/1471-2105-6-225","article-title":"Pathway level analysis of gene expression using singular value decomposition","volume":"6","author":"Tomfohr","year":"2005","journal-title":"BMC Bioinform"},{"key":"2026082813170476200_btag519-B57","doi-asserted-by":"publisher","first-page":"bbac332","DOI":"10.1093\/bib\/bbac332","article-title":"Scoring personalized molecular portraits identify Systemic Lupus Erythematosus subtypes and predict individualized drug responses, symptomatology and disease progression","volume":"23","author":"Toro-Dom\u00ednguez","year":"2022","journal-title":"Brief Bioinform"},{"key":"2026082813170476200_btag519-B58","doi-asserted-by":"publisher","first-page":"bbaf684","DOI":"10.1093\/bib\/bbaf684","article-title":"Benchmarking single-sample gene set scoring methods for application in precision medicine","volume":"26","author":"Toro-Dom\u00ednguez","year":"2025","journal-title":"Brief Bioinform"},{"key":"2026082813170476200_btag519-B59","doi-asserted-by":"publisher","first-page":"961","DOI":"10.1111\/j.1541-0420.2006.00578.x","article-title":"Generalized additive modeling with implicit variable selection by likelihood-based boosting","volume":"62","author":"Tutz","year":"2006","journal-title":"Biometrics"},{"key":"2026082813170476200_btag519-B60","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1002\/cpt.2350","article-title":"An evidence-based framework for evaluating pharmacogenomics knowledge for personalized medicine","volume":"110","author":"Whirl-Carrillo","year":"2021","journal-title":"Clin Pharmacol Ther"},{"key":"2026082813170476200_btag519-B61","doi-asserted-by":"publisher","first-page":"995972","DOI":"10.3389\/fendo.2022.995972","article-title":"GPI: an indicator for immune infiltrates and prognosis of human breast cancer from a comprehensive analysis","volume":"13","author":"Zeng","year":"2022","journal-title":"Front Endocrinol"},{"key":"2026082813170476200_btag519-B62","first-page":"562","author":"Zhang","year":"2004"},{"key":"2026082813170476200_btag519-B63","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","article-title":"Regularization and variable selection via the elastic net","volume":"67","author":"Zou","year":"2005","journal-title":"J R Stat Soc Ser B"},{"key":"2026082813170476200_btag519-B64","doi-asserted-by":"publisher","first-page":"5146","DOI":"10.1093\/bioinformatics\/btz447","article-title":"Gene set enrichment for reproducible science: comparison of CERNO and eight other algorithms","volume":"35","author":"Zyla","year":"2019","journal-title":"Bioinformatics"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btag519\/69950983\/btag519.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/42\/8\/btag519\/69950983\/btag519.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/42\/8\/btag519\/69950983\/btag519.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T17:17:14Z","timestamp":1787937434000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/doi\/10.1093\/bioinformatics\/btag519\/8741439"}},"subtitle":[],"editor":[{"given":"Jianlin","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,7,24]]},"references-count":64,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2026,8,3]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btag519","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2026,8]]},"published":{"date-parts":[[2026,7,24]]},"article-number":"btag519"}}