{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T13:03:26Z","timestamp":1790255006788,"version":"4.1.0"},"reference-count":46,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T00:00:00Z","timestamp":1790208000000},"content-version":"vor","delay-in-days":23,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["HG012124"],"award-info":[{"award-number":["HG012124"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["CA214845"],"award-info":[{"award-number":["CA214845"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["CA217694"],"award-info":[{"award-number":["CA217694"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["CA008748"],"award-info":[{"award-number":["CA008748"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Reliable translation of microRNA (miRNA) sequencing data depends on effective harmonization to mitigate artifacts from variable experimental handling. Although many harmonization methods exist, prior evaluations have focused mainly on differential expression analysis, leaving the impact on subgroup discovery understudied. We present a framework for evaluating miRNA sequencing data harmonization in the context of sample clustering that integrates artificial intelligence (AI)-augmented datasets, statistical evaluation pipelines, and accessible software tools, enabling systematic comparisons across diverse signal-to-artifact ratios and cluster-composition settings. Using this framework, we show that harmonization can, often partially, mitigate artifact-associated losses in clustering accuracy, especially at moderate signal-to-artifact ratios, with the extent of mitigation depending on the specific harmonization method, the paired clustering technique, and the cluster-composition setting. We further confirm these findings by analyzing reconstructed cohorts from The Cancer Genome Atlas breast cancer miRNA sequencing data. Collectively, these results underscore the need for tailored harmonization to support reliable subgroup discovery and highlight the broader importance of context-specific workflows in translational genomics.<\/jats:p>","DOI":"10.1093\/bib\/bbag514","type":"journal-article","created":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T11:37:45Z","timestamp":1788521865000},"source":"Crossref","is-referenced-by-count":0,"title":["Contextual evaluation of microRNA sequencing data harmonization for sample clustering"],"prefix":"10.1093","volume":"27","author":[{"given":"Jian","family":"Zou","sequence":"first","affiliation":[{"name":"Department of Statistics, School of Public Health, Chongqing Medical University , No. 1 Yixueyuan Road, Yuzhong District, Chongqing 400016,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yannick","family":"D\u00fcren","sequence":"additional","affiliation":[{"name":"Department of Mathematical Statistics, Ruhr-University Bochum , Universit\u00e4tsstra\u00dfe 150, 44801 Bochum ,","place":["Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyi","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center , 633 Third Avenue, New York, NY 10017 ,","place":["United States"]},{"name":"Department of Statistics, The University of California , Davis, One Shields Avenue, Davis, CA 95616 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Xiang","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center , 633 Third Avenue, New York, NY 10017 ,","place":["United States"]},{"name":"Department of Statistics, University of Iowa , 20 East Washington Street, 241 Schaeffer Hall, Iowa City, IA 52242-1409 ,","place":["United 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