{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T23:27:40Z","timestamp":1780615660607,"version":"3.54.1"},"reference-count":33,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2024,2,2]],"date-time":"2024-02-02T00:00:00Z","timestamp":1706832000000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2662023XXPY003"],"award-info":[{"award-number":["2662023XXPY003"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,2,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Classification of samples using biomedical omics data is a widely used method in biomedical research. However, these datasets often possess challenging characteristics, including high dimensionality, limited sample sizes, and inherent biases across diverse sources. These factors limit the performance of traditional machine learning models, particularly when applied to independent datasets.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>To address these challenges, we propose a novel classifier, Deep Centroid, which combines the stability of the nearest centroid classifier and the strong fitting ability of the deep cascade strategy. Deep Centroid is an ensemble learning method with a multi-layer cascade structure, consisting of feature scanning and cascade learning stages that can dynamically adjust the training scale. We apply Deep Centroid to three precision medicine applications\u2014cancer early diagnosis, cancer prognosis, and drug sensitivity prediction\u2014using cell-free DNA fragmentations, gene expression profiles, and DNA methylation data. Experimental results demonstrate that Deep Centroid outperforms six traditional machine learning models in all three applications, showcasing its potential in biological omics data classification. Furthermore, functional annotations reveal that the features scanned by the model exhibit biological significance, indicating its interpretability from a biological perspective. Our findings underscore the promising application of Deep Centroid in the classification of biomedical omics data, particularly in the field of precision medicine.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>Deep Centroid is available at both github (github.com\/xiexiexiekuan\/DeepCentroid) and Figshare (https:\/\/figshare.com\/articles\/software\/Deep_Centroid_A_General_Deep_Cascade_Classifier_for_Biomedical_Omics_Data_Classification\/24993516).<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae039","type":"journal-article","created":{"date-parts":[[2024,2,2]],"date-time":"2024-02-02T14:31:04Z","timestamp":1706884264000},"source":"Crossref","is-referenced-by-count":3,"title":["Deep centroid: a general deep cascade classifier for biomedical omics data classification"],"prefix":"10.1093","volume":"40","author":[{"given":"Kuan","family":"Xie","sequence":"first","affiliation":[{"name":"Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University , Wuhan 430070, People\u2019s Republic of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-2048-107X","authenticated-orcid":false,"given":"Yuying","family":"Hou","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University , Wuhan 430070, People\u2019s Republic of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1234-1091","authenticated-orcid":false,"given":"Xionghui","family":"Zhou","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University , Wuhan 430070, People\u2019s Republic of China"},{"name":"Key Laboratory of Smart Farming for Agricultural Animals, Huazhong Agricultural University , Wuhan 430070, People\u2019s Republic of China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,2,1]]},"reference":[{"key":"2024021117492717400_btae039-B1","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1038\/s41592-021-01360-8","article-title":"Deep learning and protein structure modeling","volume":"19","author":"Baek","year":"2022","journal-title":"Nat Methods"},{"key":"2024021117492717400_btae039-B2","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1049\/trit.2019.0028","article-title":"Deep learning approach for microarray cancer data classification","volume":"5","author":"Basavegowda","year":"2020","journal-title":"CAAI Trans Intell Technol"},{"key":"2024021117492717400_btae039-B3","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1186\/1471-2105-14-64","article-title":"Improved shrunken centroid classifiers for high-dimensional class-imbalanced data","volume":"14","author":"Blagus","year":"2013","journal-title":"BMC Bioinformatics"},{"key":"2024021117492717400_btae039-B4","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1080\/14737159.2020.1860021","article-title":"Concentration of cell-free DNA in different tumor types","volume":"21","author":"Bryzgunova","year":"2021","journal-title":"Expert Rev Mol Diagn"},{"key":"2024021117492717400_btae039-B5","first-page":"785","author":"Chen","year":"2016"},{"key":"2024021117492717400_btae039-B6","doi-asserted-by":"crossref","first-page":"eadg7492","DOI":"10.1126\/science.adg7492","article-title":"Accurate proteome-wide missense variant effect prediction with AlphaMissense","volume":"381","author":"Cheng","year":"2023","journal-title":"Science"},{"key":"2024021117492717400_btae039-B7","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1093\/bib\/bbz152","article-title":"DTI-CDF: a Cascade deep Forest model towards the prediction of drug\u2013target interactions based on hybrid features","volume":"22","author":"Chu","year":"2021","journal-title":"Brief Bioinform"},{"key":"2024021117492717400_btae039-B8","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach Learn"},{"key":"2024021117492717400_btae039-B9","doi-asserted-by":"crossref","first-page":"e1008671","DOI":"10.1371\/journal.pcbi.1008671","article-title":"Hands-on training about overfitting","volume":"17","author":"Dem\u0161ar","year":"2021","journal-title":"PLoS Comput Biol"},{"key":"2024021117492717400_btae039-B10","doi-asserted-by":"crossref","first-page":"3207","DOI":"10.1158\/1078-0432.CCR-06-2765","article-title":"Strong time dependence of the 76-gene prognostic signature for node-negative breast cancer patients in the TRANSBIG multicenter independent validation series","volume":"13","author":"Desmedt","year":"2007","journal-title":"Clin. 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