{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,7]],"date-time":"2026-06-07T07:14:32Z","timestamp":1780816472707,"version":"3.54.1"},"reference-count":64,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2019,11,27]],"date-time":"2019-11-27T00:00:00Z","timestamp":1574812800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Advance Queensland Research Fellowship"},{"DOI":"10.13039\/501100001793","name":"Queensland University of Technology","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001793","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Oncogenesis and cancer can arise as a consequence of a wide range of genomic aberrations including mutations, copy number alterations, expression changes and epigenetic modifications encompassing multiple omics layers. Integrating genomic, transcriptomic, proteomic and epigenomic datasets via multi-omics analysis provides the opportunity to derive a deeper and holistic understanding of the development and progression of cancer. There are two primary approaches to integrating multi-omics data: multi-staged (focused on identifying genes driving cancer) and meta-dimensional (focused on establishing clinically relevant tumour or sample classifications). A number of ready-to-use bioinformatics tools are available to perform both multi-staged and meta-dimensional integration of multi-omics data. In this study, we compared nine different integration tools using real and simulated cancer datasets. The performance of the multi-staged integration tools were assessed at the gene, function and pathway levels, while meta-dimensional integration tools were assessed based on the sample classification performance. Additionally, we discuss the influence of factors such as data representation, sample size, signal and noise on multi-omics data integration. Our results provide current and much needed guidance regarding selection and use of the most appropriate and best performing multi-omics integration tools.<\/jats:p>","DOI":"10.1093\/bib\/bbz121","type":"journal-article","created":{"date-parts":[[2019,9,4]],"date-time":"2019-09-04T19:26:49Z","timestamp":1567625209000},"page":"1920-1936","source":"Crossref","is-referenced-by-count":64,"title":["A comparative study of multi-omics integration tools for cancer driver gene identification and tumour subtyping"],"prefix":"10.1093","volume":"21","author":[{"given":"Anita","family":"Sathyanarayanan","sequence":"first","affiliation":[{"name":"School of Biomedical Sciences, Faculty of Health, and Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rohit","family":"Gupta","sequence":"additional","affiliation":[{"name":"Department of Biotechnology, Indian Institute of Technology 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