Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/180054
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dc.contributor.authorSelvarajoo, Kumaren_US
dc.contributor.authorMaurer-Stroh, Sebastianen_US
dc.date.accessioned2024-09-11T04:46:16Z-
dc.date.available2024-09-11T04:46:16Z-
dc.date.issued2024-
dc.identifier.citationSelvarajoo, K. & Maurer-Stroh, S. (2024). Towards multi-omics synthetic data integration. Briefings in Bioinformatics, 25(3). https://dx.doi.org/10.1093/bib/bbae213en_US
dc.identifier.issn1467-5463en_US
dc.identifier.urihttps://hdl.handle.net/10356/180054-
dc.description.abstractAcross many scientific disciplines, the development of computational models and algorithms for generating artificial or synthetic data is gaining momentum. In biology, there is a great opportunity to explore this further as more and more big data at multi-omics level are generated recently. In this opinion, we discuss the latest trends in biological applications based on process-driven and data-driven aspects. Moving ahead, we believe these methodologies can help shape novel multi-omics-scale cellular inferences.en_US
dc.description.sponsorshipAgency for Science, Technology and Research (A*STAR)en_US
dc.language.isoenen_US
dc.relation.ispartofBriefings in Bioinformaticsen_US
dc.rights© The Author(s) 2024. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.en_US
dc.subjectMedicine, Health and Life Sciencesen_US
dc.titleTowards multi-omics synthetic data integrationen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Biological Sciencesen_US
dc.contributor.organizationBioinformatics Institute, A*STARen_US
dc.contributor.organizationYong Loo Lin School of Medicine, NUSen_US
dc.contributor.organizationSynthetic Biology for Clinical and Technological Innovation, NUSen_US
dc.identifier.doi10.1093/bib/bbae213-
dc.description.versionPublished versionen_US
dc.identifier.pmid38711370-
dc.identifier.scopus2-s2.0-85192586359-
dc.identifier.issue3en_US
dc.identifier.volume25en_US
dc.subject.keywordsSynthetic dataen_US
dc.subject.keywordsMulti-omicsen_US
dc.description.acknowledgementThe authors thank the Bioinformatics Institute, A∗STAR, for funding and support.en_US
item.grantfulltextopen-
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