{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,24]],"date-time":"2026-05-24T12:03:30Z","timestamp":1779624210930,"version":"3.53.1"},"reference-count":32,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2026,5,24]],"date-time":"2026-05-24T00:00:00Z","timestamp":1779580800000},"content-version":"vor","delay-in-days":23,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,5,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Modern biology increasingly relies on large-scale screening to generate high dimensional datasets with potential to accelerate discovery. However, analysing these complex datasets remains challenging, due to hierarchical biological structure, uncertainty in the true number of groups, and high dimensional noise that confounds genuine biological signal. Here we present an unsupervised consensus clustering tool, Untangled, that aggregates clustering solutions across granularities to construct a stability-based representation, followed by cluster number optimization and systematic evaluation of cluster robustness. Through extensive benchmarking on simulated datasets, ground truth datasets and diverse high-dimensional screening applications, we demonstrate that Untangled reliably recovers underlying relationships, resolves stable meaningful substructure, and more effectively prioritizes robust clusters with shared biological mechanisms and conserved phenotypic responses than alternative clustering approaches. These results establish Untangled as a scalable framework for cluster discovery to guide efficient follow-up investigation from high-dimensional biological datasets.<\/jats:p>","DOI":"10.1093\/bib\/bbag242","type":"journal-article","created":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T12:14:28Z","timestamp":1777637668000},"source":"Crossref","is-referenced-by-count":0,"title":["A scalable, multi-resolution consensus clustering approach for prioritizing robust signals from high-throughput screens"],"prefix":"10.1093","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-2085-8326","authenticated-orcid":false,"given":"Shaine Chenxin","family":"Bao","sequence":"first","affiliation":[{"name":"Institute for Molecular Bioscience, The University of Queensland , 306 Carmody Road, St Lucia, Brisbane , QLD, 4072,","place":["Australia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kathleen 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Queensland , 306 Carmody Road, St Lucia, Brisbane , QLD, 4072,","place":["Australia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ian R","family":"Henderson","sequence":"additional","affiliation":[{"name":"Institute for Molecular Bioscience, The University of Queensland , 306 Carmody Road, St Lucia, Brisbane , QLD, 4072,","place":["Australia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Liu","sequence":"additional","affiliation":[{"name":"Peter MacCallum Cancer Centre , 305 Grattan Street, Melbourne, VIC, 3000 ,","place":["Australia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Evanny","family":"Marinovic","sequence":"additional","affiliation":[{"name":"Peter MacCallum Cancer Centre , 305 Grattan Street, Melbourne, VIC, 3000 ,","place":["Australia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mark S","family":"Carey","sequence":"additional","affiliation":[{"name":"Division of Gynecology Oncology, Department of Obstetrics and Gynecology, University of British Columbia , 2775 Laurel St, Vancouver, BC, V5Z 1M9 ,","place":["Canada"]},{"name":"Department of Clinical Research, BC Cancer , 600 West 10th Avenue, Vancouver, BC, V5Z 4E6 ,","place":["Canada"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ian G","family":"Campbell","sequence":"additional","affiliation":[{"name":"Peter MacCallum Cancer Centre , 305 Grattan Street, Melbourne, VIC, 3000 ,","place":["Australia"]},{"name":"The Sir Peter MacCallum Department of Oncology, The University of Melbourne , 305 Grattan Street, Parkville , VIC, 3000,","place":["Australia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaylene J","family":"Simpson","sequence":"additional","affiliation":[{"name":"Victorian Centre for Functional Genomics, Peter MacCallum Cancer Centre , 305 Grattan Street, Melbourne , VIC, 3000,","place":["Australia"]},{"name":"The Sir Peter MacCallum Department of Oncology, The University of Melbourne , 305 Grattan Street, Parkville , VIC, 3000,","place":["Australia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1170-4690","authenticated-orcid":false,"given":"Dane","family":"Cheasley","sequence":"additional","affiliation":[{"name":"Peter MacCallum Cancer Centre , 305 Grattan Street, Melbourne, VIC, 3000 ,","place":["Australia"]},{"name":"The Sir Peter MacCallum Department of Oncology, The University of Melbourne , 305 Grattan Street, Parkville , VIC, 3000,","place":["Australia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dalia","family":"Mizikovsky","sequence":"additional","affiliation":[{"name":"Institute for Molecular Bioscience, The University of Queensland , 306 Carmody Road, St Lucia, Brisbane , QLD, 4072,","place":["Australia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9334-8107","authenticated-orcid":false,"given":"Nathan 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