Normative Modelling in Psychology and Endocrinology
Using data from our labs, our research partners and from the published literature, we develop and validate age-related models that allow the assessment of individuals against the general healthy population. In addition we perform investigations and meta-analyses that use the models as a benchmark to gain new insights in the diagnosis and treatment of (for example) breast cancer, diabetes and PCOS.
Gavriel, C, Dimitriou, N, Brieu, N, Nearchou, IP, Arandelovic, O, Schmidt, G, Harrison, DJ & Caie, PD 2021, ‘Assessment of immunological features in muscle-invasive bladder cancer prognosis using ensemble learning‘, Cancers, vol. 13, no. 7, 1624. https://doi.org/10.3390/cancers13071624
Nearchou, IP, Soutar, DA, Lillard, K, Ueno, H, Arandelovic, O, Harrison, DJ & Caie, PD 2020, ‘Abstract LB-368: Applications of automated image analysis, machine learning and spatial statistics for the improvement of stage II colorectal cancer prognosis‘. https://doi.org/10.1158/1538-7445.AM2020-LB-368
Xingzhi, Y, Dimitriou, N, Caie, PD, Harrison, DJ & Arandelovic, O 2019, ‘Colorectal cancer outcome prediction from H&E whole slide images using machine learning and automatically inferred phenotype profiles‘, ArXiv e-prints.
Yue, X, Dimitriou, N, Caie, P, Harrison, D & Arandjelovic, O 2019, Colorectal cancer outcome prediction from H&E whole slide images using machine learning and automatically inferred phenotype profiles. in O Eulenstein, H Al-Mubaid & Q Ding (eds), Proceedings of 11th International Conference on Bioinformatics and Computational Biology, BICOB 2019: Honolulu; United States; 18 March 2019 through 20 March 2019. EPiC Series in Computing, vol. 60, EasyChair, pp. 139-127, 11th International Conference on Bioinformatics and Computational Biology (BICOB), Honolulu, United States, 18/03/19. https://doi.org/10.29007/n912