I am a third-year PhD student at the BSU, supervised by Dr Paul Kirk.
My research is concerned with the development of statistical methodology for the analysis of genomic data in stratified medicine. The identification of relevant patient subgroups (e.g. patients that might be expected to respond similarly to treatments, or to have similar disease progression/outcomes) on the basis of genomics datasets presents a number of challenges. In particular, because genomics datasets typically comprise measurements taken on a very large number of variables (e.g. whole-genome expression data), it is usually the case that we can identify many different patient subgroups, depending on which variables we include in our analysis. In my PhD, I am developing and implementing methods that integrate genomic datasets with data on specific patient outcomes, to ensure that we identify truly relevant patient subgroups.
To find out more about my work, please visit my personal website.
Selected PapersAlessandra Cabassi, Davide Pigoli, Piercesare Secchi, and Patrick A. Carter (2017)Permutation tests for the equality of covariance operators of functional data with applications to evolutionary biology
Electronic Journal of Statistics Vol 11, No 2: 3815-3840
Alessandra Cabassi, Alessandro Casa, Matteo Fontana, Massimiliano Russo, and Alessio Farcomeni (2018)Three Testing Perspectives on Connectome Data
In: Canale A., et al. (eds) Studies in Neural Data Science. Springer Proceedings in Mathematics & Statistics Vol 257: 37-56
Alessandra Cabassi, Paul DW Kirk (2020)Multiple kernel learning for integrative consensus clustering of ’omic datasets
Denis Seyres, Alessandra Cabassi, ..., Paul DW Kirk, Mattia Frontini (2020)Extreme phenotypes define epigenetic and metabolic signatures in cardiometabolic syndrome
bioRxiv : 2020.03.06.961805v1