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dc.contributor.authorVitali, Francesca
dc.contributor.authorLi, Qike
dc.contributor.authorSchissler, A Grant
dc.contributor.authorBerghout, Joanne
dc.contributor.authorKenost, Colleen
dc.contributor.authorLussier, Yves A
dc.date.accessioned2019-09-06T23:19:54Z
dc.date.available2019-09-06T23:19:54Z
dc.date.issued2019-05-21
dc.identifier.citationFrancesca Vitali, Qike Li, A Grant Schissler, Joanne Berghout, Colleen Kenost, Yves A Lussier, Developing a ‘personalome’ for precision medicine: emerging methods that compute interpretable effect sizes from single-subject transcriptomes, Briefings in Bioinformatics, Volume 20, Issue 3, May 2019, Pages 789–805, https://doi.org/10.1093/bib/bbx149en_US
dc.identifier.issn1467-5463
dc.identifier.pmid29272327
dc.identifier.doi10.1093/bib/bbx149
dc.identifier.urihttp://hdl.handle.net/10150/634137
dc.description.abstractThe development of computational methods capable of analyzing -omics data at the individual level is critical for the success of precision medicine. Although unprecedented opportunities now exist to gather data on an individual's -omics profile (personalome'), interpreting and extracting meaningful information from single-subject -omics remain underdeveloped, particularly for quantitative non-sequence measurements, including complete transcriptome or proteome expression and metabolite abundance. Conventional bioinformatics approaches have largely been designed for making population-level inferences about average' disease processes; thus, they may not adequately capture and describe individual variability. Novel approaches intended to exploit a variety of -omics data are required for identifying individualized signals for meaningful interpretation. In this review-intended for biomedical researchers, computational biologists and bioinformaticians-we survey emerging computational and translational informatics methods capable of constructing a single subject's personalome' for predicting clinical outcomes or therapeutic responses, with an emphasis on methods that provide interpretable readouts. Key points: (i) the single-subject analytics of the transcriptome shows the greatest development to date and, (ii) the methods were all validated in simulations, cross-validations or independent retrospective data sets. This survey uncovers a growing field that offers numerous opportunities for the development of novel validation methods and opens the door for future studies focusing on the interpretation of comprehensive personalomes' through the integration of multiple -omics, providing valuable insights into individual patient outcomes and treatments.en_US
dc.description.sponsorshipNational Institute of Health (NIH)/Office of the Director Precision Medicine Initiative [1UG3OD023171-01]; Precision Medicine Initiative of the Center for Biomedical Informatics and Biostatistics of the University of Arizona Health Sciences; NIH/National Heart, Lung, and Blood Institute [HL126609-01, HL132523, U01 HL125208]; NIH/National Cancer Institute [P30CA023074, 1R01CA190696-01]; NIH/National Institute of Allergy and Infectious Diseases [U01AI122275-01]en_US
dc.language.isoenen_US
dc.publisherOXFORD UNIV PRESSen_US
dc.rights© The Author 2017. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://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.subjectn-of-1en_US
dc.subjectpersonalomeen_US
dc.subjectprecision medicineen_US
dc.subjectsingle-subject studiesen_US
dc.titleDeveloping a 'personalome' for precision medicine: emerging methods that compute interpretable effect sizes from single-subject transcriptomesen_US
dc.typeArticleen_US
dc.contributor.departmentUniv Arizona, Ctr Biomed Informat & Biostaten_US
dc.identifier.journalBRIEFINGS IN BIOINFORMATICSen_US
dc.description.noteOpen access articleen_US
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en_US
dc.eprint.versionFinal published versionen_US
dc.source.journaltitleBriefings in bioinformatics
refterms.dateFOA2019-09-06T23:19:55Z


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