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Random forest modelling of neuropathological features identifies microglial activation as an accurate pathological classifier of C9orf72-related amyotrophic lateral sclerosis

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dc.contributor.authorRifai, Olivia M.
dc.contributor.authorLongden, James
dc.contributor.authorO’Shaughnessy, Judi
dc.contributor.authorSewell, Michael D.E.
dc.contributor.authorMcDade, Karina
dc.contributor.authorDaniels, Michael J.D.
dc.contributor.authorAbrahams, Sharon
dc.contributor.authorChandran, Siddharthan
dc.contributor.authorMcColl, Barry
dc.contributor.authorSibley, Christopher R.
dc.contributor.authorGregory, Jenna M.
dc.contributor.institutionUniversity of Aberdeen.Medical Sciencesen
dc.contributor.institutionUniversity of Aberdeen.Neuroscienceen
dc.date.accessioned2022-08-01T10:05:01Z
dc.date.available2022-08-01T10:05:01Z
dc.date.issued2021-12-10
dc.descriptionAcknowledgments This research was funded in part by a studentship from the Wellcome Trust (108890/Z/15/Z) to OMR and MDES, a Pathological Society and Jean Shanks foundation grant (217CHA R46564) to JMG and JO, and a Sir Henry Dale fellowship jointly funded by the Wellcome Trust and the Royal Society (215454/Z/19/Z) to CRS. We gratefully acknowledge Dr. Tom Gillingwater for his helpful comments and support. This work would also not be possible without the resources of the Edinburgh Brain Bank. The authors declare no conflicts of interest. SD numbers of cases from the Edinburgh Brain Bank included in the study are available upon request.en
dc.format.extent5102435
dc.identifier306036714
dc.identifier9f114434-3249-428e-ba21-900e99d0c97f
dc.identifier.citationRifai, O M, Longden, J, O’Shaughnessy, J, Sewell, M D E, McDade, K, Daniels, M J D, Abrahams, S, Chandran, S, McColl, B, Sibley, C R & Gregory, J M 2021 'Random forest modelling of neuropathological features identifies microglial activation as an accurate pathological classifier of C9orf72-related amyotrophic lateral sclerosis' bioRxiv, bioRxiv. https://doi.org/10.1101/2021.12.10.471808en
dc.identifier.doi10.1101/2021.12.10.471808
dc.identifier.otherORCID: /0000-0003-3337-4079/work/113328250
dc.identifier.otherBibtex: Rifai2021.12.10.471808
dc.identifier.urihttps://hdl.handle.net/2164/18977
dc.language.isoeng
dc.publisherbioRxiv
dc.relation.ispartofseriesbioRxiven
dc.subjectAmyotrophic lateral sclerosisen
dc.subjectfrontotemporal dementiaen
dc.subjectC9orf72en
dc.subjectneuroinflammationen
dc.subjectmicrogliaen
dc.subjectpost-mortem tissueen
dc.subjectRC0321 Neuroscience. Biological psychiatry. Neuropsychiatryen
dc.subjectWellcome Trusten
dc.subject108890/Z/15/Zen
dc.subject215454/Z/19/Zen
dc.subject.lccRC0321en
dc.titleRandom forest modelling of neuropathological features identifies microglial activation as an accurate pathological classifier of C9orf72-related amyotrophic lateral sclerosisen
dc.typePreprinten

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