Author

Matthew A. Clarke

University College LondonORCID 0000-0002-2582-9689Unclaimed 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· 3 Research Focus:drug response / drug discovery· 2 Research Focus:gene regulatory network inference· 2 Approach:statistical model· 2 Assay:bulk RNA-seq· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Approach:graph neural network· 1

Frequent Co-Authors

Jasmin FisheriD· 3 shared Jyoti Sharma ChoudharyiD· 2 shared Amna Z. ShahiD· 1 shared Ann‐Kathrin ReuschliD· 1 shared Ashley NichollsiD· 1 shared Benny ChainiD· 1 shared Charlie George BarkeriD· 1 shared Charlie George BarkeriD· 1 shared

Activity by Year

2022-2025 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 22 mutual co-author links

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3 shared papersSystems Biology

Jasmin FisherORCID

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3 papers

Recent·Most cited

Systems BiologybioRxivPreprintMay 16, 2025

MAGELLAN: Automated Generation of Interpretable Computational Models for Biological Reasoning

Matthew A. Clarke, Charlie George Barker, Yuxin Sun…·Jasmin Fisher

MAGELLAN constructs interpretable computational models from pathway data and experimental rules, demonstrating predictive accuracy in breast cancer and NSCLC signalling.

DOI 10.1101/2025.05.16.653408

humangraph neural network

Cancer BiologybioRxivPreprintJan 7, 2025

Predicting Personalised Therapeutic Combinations in Non-Small Cell Lung Cancer Using In Silico Modelling

Matthew A. Clarke, Charlie George Barker, Ashley Nicholls…·Jasmin Fisher

This paper develops an in silico model to predict personalized therapeutic combinations for NSCLC by simulating genetic profiles and testing drug combinations to overcome resistance.

2 citations

humanbulk RNA-seq

Systems Biologynpj Digital MedicinePeer-reviewedFeb 14, 2022

Executable Network of SARS-CoV-2-Host Interaction Predicts Drug Combination Treatments

Rowan S. M. Howell, Matthew A. Clarke, Ann‐Kathrin Reuschl…·Jasmin Fisher

This paper predicts effective drug combinations for treating early- and late-stage severe SARS-CoV-2 infection using an executable network model of host-virus interactions.

16 citations

humanstatistical model