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dc.contributor.authorCaie, Peter D
dc.contributor.authorHarrison, David J
dc.contributor.editorSchmitz, Ulf
dc.contributor.editorWolkenhauer, Olaf
dc.date.accessioned2017-01-02T00:33:04Z
dc.date.available2017-01-02T00:33:04Z
dc.date.issued2016
dc.identifier.citationCaie , P D & Harrison , D J 2016 , Next-generation pathology . in U Schmitz & O Wolkenhauer (eds) , Systems Medicine . Methods in Molecular Biology , vol. 1386 , Humana Press , pp. 61-72 . https://doi.org/10.1007/978-1-4939-3283-2_4en
dc.identifier.isbn9781493932825
dc.identifier.isbn9781493932832
dc.identifier.otherPURE: 240275801
dc.identifier.otherPURE UUID: 4d785115-f346-4b04-a973-4e643d781cf8
dc.identifier.otherPubMed: 26677179
dc.identifier.otherScopus: 84950238992
dc.identifier.otherORCID: /0000-0002-0031-9850/work/60196552
dc.identifier.otherWOS: 000368813900005
dc.identifier.otherORCID: /0000-0001-9041-9988/work/64034291
dc.identifier.urihttps://hdl.handle.net/10023/10033
dc.description.abstractThe field of pathology is rapidly transforming from a semiquantitative and empirical science toward a big data discipline. Large data sets from across multiple omics fields may now be extracted from a patient's tissue sample. Tissue is, however, complex, heterogeneous, and prone to artifact. A reductionist view of tissue and disease progression, which does not take this complexity into account, may lead to single biomarkers failing in clinical trials. The integration of standardized multi-omics big data and the retention of valuable information on spatial heterogeneity are imperative to model complex disease mechanisms. Mathematical modeling through systems pathology approaches is the ideal medium to distill the significant information from these large, multi-parametric, and hierarchical data sets. Systems pathology may also predict the dynamical response of disease progression or response to therapy regimens from a static tissue sample. Next-generation pathology will incorporate big data with systems medicine in order to personalize clinical practice for both prognostic and predictive patient care.
dc.format.extent12
dc.language.isoeng
dc.publisherHumana Press
dc.relation.ispartofSystems Medicineen
dc.relation.ispartofseriesMethods in Molecular Biologyen
dc.rights© 2016, Publisher / the Author(s). This work is made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at www.springer.com / https://dx.doi.org/10.1007/978-1-4939-3283-2_4en
dc.subjectHistopathologyen
dc.subjectIntegrative pathologyen
dc.subjectSystems pathologyen
dc.subjectSpatial heterogeneityen
dc.subjectPredictive modelsen
dc.subjectCancer pathologyen
dc.subjectMulti-omicsen
dc.subjectImage analysisen
dc.subjectRB Pathologyen
dc.subjectRC0254 Neoplasms. Tumors. Oncology (including Cancer)en
dc.subjectSDG 3 - Good Health and Well-beingen
dc.subject.lccRBen
dc.subject.lccRC0254en
dc.titleNext-generation pathologyen
dc.typeBook itemen
dc.description.versionPostprinten
dc.contributor.institutionUniversity of St Andrews. School of Medicineen
dc.identifier.doihttps://doi.org/10.1007/978-1-4939-3283-2_4
dc.date.embargoedUntil2017-01-01


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