Author

Richard Stratford

Oslo Cancer Cluster

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 2 Immunology· 1

Signature Assays, Organisms & Methods

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Research Focus:differential expression· 3 Organism:human· 2 Assay:proteomics· 2 Research Focus:cell type annotation· 1 Approach:deep learning· 1 Research Focus:drug response / drug discovery· 1 Approach:machine learning (classical)· 1 Organism:mouse· 1

Frequent Co-Authors

Trevor ClancyiD· 3 shared Boris SimovskiiD· 2 shared Brandon MaloneiD· 2 shared Ioannis VardaxisiD· 2 shared Marius GheorgheiD· 2 shared Alexandru OdainiciD· 1 shared Clément MolinéiD· 1 shared Hassen KarediD· 1 shared

Activity by Year

2020-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 33 mutual co-author links

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3 shared papersBioinformatics

Trevor ClancyORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Machine Learning-Driven Antigen Selection Reveals Conserved T-Cell Targets for Broad Coronavirus Vaccination

Lorenzo Federico, Alexandru Odainic, Katrine Persgård Lund, Ingrid Marie Egner, Katrin E. Wiese, Lisette A. H. M. Cornelissen, Hassen Kared, Richard Stratford, Sebastian Kapell, Brandon Malone, Marius Gheorghe, Pierre Machart, Raman Siarheyeu, Yuki Tanaka, Trevor Clancy, , Ludvig A. Munthe

This study identifies conserved T-cell targets across betacoronaviruses using machine learning and validates their immunogenicity through functional assays and mRNA vaccination in mice.

bioRxiv · Apr 3, 2026 · PreprintImmunology

Deep learning of antibody epitopes using molecular permutation vectors

Ioannis Vardaxis, Boris Simovski, Irantzu Anzar, Richard Stratford, Trevor Clancy

This paper introduces a deep learning method for predicting B cell epitopes using molecular permutation vectors and 3D protein structures, showing improved accuracy over existing tools.

bioRxiv · Mar 20, 2024 · PreprintBioinformatics

Artificial intelligence predicts the immunogenic landscape of SARS-CoV-2: toward universal blueprints for vaccine designs

Brandon Malone, Boris Simovski, Clément Moliné, Jun Cheng, Marius Gheorghe, Hugues Fontenelle, Ioannis Vardaxis, Simen Tennøe, J-A Malmberg, Richard Stratford, Trevor Clancy

This study uses AI to predict immunogenic epitopes across SARS-CoV-2 variants, identifying vaccine candidates with broad population coverage.

Scientific Reports · Dec 23, 2020Peer-reviewedBioinformatics