Rifai, Olivia M.Longden, JamesO'Shaughnessy, JudiSewell, Michael D.E.Pate, JudithMcDade, KarinaDaniels, Michael J.D.Abrahams, SharonChandran, SiddharthanMcColl, Barry W.Sibley, Christopher R.Gregory, Jenna M.2023-06-212023-06-212022-12Rifai, O M, Longden, J, O'Shaughnessy, J, Sewell, M D E, Pate, J, McDade, K, Daniels, M J D, Abrahams, S, Chandran, S, McColl, B W, Sibley, C R & Gregory, J M 2022, 'Random forest modelling demonstrates microglial and protein misfolding features to be key phenotypic markers in C9orf72-ALS', The Journal of pathology, vol. 258, no. 4, pp. 366-381. https://doi.org/10.1002/path.60080022-3417Bibtex: https://doi.org/10.1002/path.6008ORCID: /0000-0003-3337-4079/work/122953770https://hdl.handle.net/2164/20950Acknowledgments We gratefully acknowledge Professor Tom Gillingwater for his helpful comments and support. This work would not have been possible without the resources of the Edinburgh Brain Bank, and the people with ALS and their families who have generously donated tissue. 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.1625542426engAmyotrophic lateral sclerosisfrontotemporal dementiaC9orf72neuroinflammationmicrogliapost-mortem tissueTDP-43FUSmachine learningdigital pathologyRB PathologyWellcome Trust108890/Z/15/ZOther215454/Z/19/ZSupplementary InformationRBJournal article10.1002/path.6008https://onlinelibrary.wiley.com/doi/abs/10.1002/path.60082584