Author

S. T. M. Ball

University of Liverpool

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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Physiology· 3

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Assay:bulk RNA-seq· 1 Research Focus:cell type annotation· 1 Approach:contrastive learning· 1 Research Focus:database / resource· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Assay:electrophysiology· 1

Frequent Co-Authors

R. Barrett-Jolley· 2 shared Anne McArdleiD· 1 shared Caroline Amy StauntoniD· 1 shared E. Sayari· 1 shared F. O'Brien· 1 shared L. Abdul Kadir· 1 shared Malcolm Joseph JacksoniD· 1 shared N. Celik· 1 shared

Activity by Year

2020-2024 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 17 mutual co-author links

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2 shared papersPhysiology

R. Barrett-Jolley

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Author profile →

3 papers

Recent·Most cited

Peroxiredoxin 2 mediates redox-stimulated adaptations to oxidative phosphorylation induced by contractile activity in human skeletal muscle myotubes

Robert A. Heaton, S. T. M. Ball, Caroline Amy Staunton, Vincent Mouly, Samantha W. Jones, Anne McArdle, Malcolm Joseph Jackson

This study shows that PRDX2 mediates redox-stimulated adaptations to oxidative phosphorylation in human skeletal muscle myotubes exposed to contractile activity or H2O2.

Free Radical Biology and Medicine · Dec 4, 2024Peer-reviewedPhysiology

DeepGANnel: Synthesis of fully annotated single molecule patch-clamp data using generative adversarial networks

S. T. M. Ball, N. Celik, E. Sayari, L. Abdul Kadir, F. O'Brien, R. Barrett-Jolley

This paper develops DeepGANnel, a GAN-based method to generate synthetic single molecule patch-clamp data with labels, addressing the lack of large training datasets for automated analysis tools.

bioRxiv · Jun 25, 2020Physiology

Discriminant Analysis of Principle Component analyses of Physiological Data.

O. Haidar, S. T. M. Ball, R. Barrett-Jolley

The paper compares discriminant analysis of principle component (DAPC) to PCA for analyzing multivariate physiological data, finding DAPC more accurate in detecting differences between synthetic datasets.

bioRxiv · Jan 9, 2020 · PreprintPhysiology