Author

Riccardo Masina

University of CambridgeORCID 0000-0002-9236-616XUnclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Organism:human· 2 Approach:statistical model· 2 Approach:Bayesian inference· 1 Research Focus:benchmarking· 1 Assay:bulk RNA-seq· 1 Research Focus:differential expression· 1 Assay:DNA methylation· 1 Research Focus:protein structure prediction· 1

Frequent Co-Authors

Oscar M. RuedaiD· 2 shared Stephen‐John SammutiD· 2 shared Alistair G. MartiniD· 1 shared Anja BrouweriD· 1 shared Carlos CaldasiD· 1 shared Dana W.Y. TsuiiD· 1 shared Davina Gillian GaleiD· 1 shared Dom KirkhamiD· 1 shared

Activity by Year

2023-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 38 mutual co-author links

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2 shared papersBioinformatics

Oscar M. RuedaORCID

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2 papers

Recent·Most cited

Interventionally-guided representation learning for robust and interpretable AI models in cancer medicine

Dom Kirkham, Riccardo Masina, Stephen‐John Sammut, Sach Mukherjee, Oscar M. Rueda

This paper introduces a method that uses causal signals from genetic screens to improve the robustness and interpretability of AI models for cancer medicine, showing effective generalization across data and biological contexts.

bioRxiv · Jul 21, 2025 · PreprintBioinformatics

A large-scale retrospective study in metastatic breast cancer patients using circulating tumor DNA and machine learning to predict treatment outcome and progression-free survival

Emma J. Beddowes, Mario Ortega Duran, Solon Karapanagiotis, Alistair G. Martin, Meiling Gao, Riccardo Masina, Ramona Woitek, James Tanner, Fleur Tippin, Justine Kane, Jonathan Lay, Anja Brouwer, Stephen‐John Sammut, Suet‐Feung Chin, Davina Gillian Gale, , Carlos Caldas

This study uses ctDNA from metastatic breast cancer patients and a Bayesian model to predict treatment response and progression-free survival, outperforming alternative methods.

bioRxiv · Mar 3, 2023 · PreprintGenomics