Author

Alexandra L. Young

University College LondonORCID 0000-0002-7772-781XUnclaimed ORCID profile · follow to get notified of new preprints

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Approach:statistical model· 6 Organism:human· 5 Assay:imaging· 3 Research Focus:benchmarking· 2 Research Focus:clustering· 2 Research Focus:differential expression· 2 Research Focus:trajectory inference· 2 Assay:bulk RNA-seq· 1

Frequent Co-Authors

Daniel C. AlexanderiD· 6 shared Neil P. OxtobyiD· 6 shared Nicholas C. FirthiD· 3 shared André AltmanniD· 2 shared Arman EshaghiiD· 2 shared David M. CashiD· 2 shared Frederik BarkhofiD· 2 shared John C. MorrisiD· 2 shared

Activity by Year

2018-2025 · peak 4/yr

Co-Author Collaboration Network

10 top collaborators · 32 mutual co-author links

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6 shared papersNeuroscience

Daniel C. AlexanderORCID

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Author profile →

7 papers

Recent·Most cited

Parsimonious EBM: generalising the event-based model of disease progression for simultaneous events

Christopher S Parker, Neil P. Oxtoby, Alexandra L. Young, Alzheimer's Disease Neuroimaging Initiative, DC Alexander, Hui Gary Zhang

This paper introduces the parsimonious event-based model (P-EBM) to infer disease progression sequences with fewer stages than biomarkers, showing improved performance in Alzheimer's disease analysis.

NeuroImage · Mar 19, 2025Peer-reviewedNeuroscience

Tau-first subtype of Alzheimer's disease consistently identified across in vivo and post mortem studies

Leon Aksman, Neil P. Oxtoby, Marzia A. Scelsi, Peter A Wijeratne, Alexandra L. Young, Isadora Lopes Alves, Lyduine Eisa Collij, Jacob W. Vogel, Frederik Barkhof, Daniel C. Alexander, André Altmann, Alzheimer’s Disease Neuroimaging Initiative

The paper identifies a tau-first subtype of Alzheimer's disease through analysis of post mortem and in vivo data, showing higher soluble TREM2 levels in tau-first cases.

Brain · Jul 11, 2023Peer-reviewedNeuroscience

pySuStaIn: a Python implementation of the Subtype and Stage Inference algorithm

Leon Aksman, Peter A Wijeratne, Neil P. Oxtoby, Arman Eshaghi, Cameron Shand, André Altmann, Daniel C. Alexander, Alexandra L. Young

pySuStaIn provides a Python implementation of the SuStaIn algorithm to infer disease subtypes and stages from cross-sectional data.

SoftwareX · Sep 27, 2021Peer-reviewedNeuroscience

Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference

Alexandra L. Young, Razvan Valentin Marinescu, Neil P. Oxtoby, Martina Bocchetta, Keir X. X. Yong, Nicholas C. Firth, David M. Cash, David Lee Thomas, Katrina M. Dick, M. Jorge Cardoso, John Cornelis Van Swieten, Barbara Borroni, Daniela Galimberti, Mario Masellis, Maria Carmela Tartaglia, , Ronald G. Thomas

This paper introduces SuStaIn, a machine learning method that identifies disease subtypes and their temporal progression from cross-sectional data, improving prediction of diagnostic conversion in neurodegenerative diseases.

Nature Communications · Oct 9, 2018Peer-reviewedNeuroscience

Non-Parametric Mixture Modelling and its Application to Disease Progression Modelling

Nicholas C. Firth, Neil P. Oxtoby, Silvia Primativo, Emilie Brotherhood, Alexandra L. Young, Keir X. X. Yong, Sebastian James Crutch, Daniel C. Alexander

This paper introduces KDEMM for disease progression modeling, showing it outperforms GMMs in simulating cognitive data and aligns better with clinical phenotypes in Alzheimer's subtypes.

bioRxiv · Apr 10, 2018 · PreprintNeuroscience

Progression of regional grey matter atrophy in multiple sclerosis

Arman Eshaghi, Razvan Valentin Marinescu, Alexandra L. Young, Nicholas C. Firth, Ferrán Prados, M. Jorge Cardoso, Carmen Tur, Floriana De Angelis, Niamh Cawley, Wallace J. Brownlee, Nicola De Stefano, Maria Laura Stromillo, Marco Battaglini, Serena Ruggieri, Claudio Gasperini, , Jacqueline A. Palace

This study identifies the sequence of grey matter atrophy progression in multiple sclerosis using longitudinal MRI data and shows its association with disability accumulation in relapsing-remitting MS.

Brain · Mar 16, 2018Peer-reviewedNeuroscience

Data-driven models of dominantly-inherited Alzheimer’s disease progression

Neil P. Oxtoby, Alexandra L. Young, David M. Cash, Tammie L.S. Benzinger, Anne M. Fagan, John C. Morris, Randall John Bateman, Nick C. Fox, Jonathan M. Schott, Daniel C. Alexander

This paper develops data-driven models to predict Alzheimer’s disease progression in dominantly-inherited cases, showing improved accuracy over traditional estimates of years until onset.

Brain · Feb 8, 2018Peer-reviewedNeuroscience