Author

Edward O. Pyzer‐Knapp

IBM (United Kingdom)ORCID 0000-0002-8232-8282Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Approach:machine learning (classical)· 3 Research Focus:benchmarking· 2 Research Focus:differential expression· 2 Assay:metagenomics· 2 Organism:Arabidopsis· 1 Research Focus:clustering· 1 Research Focus:database / resource· 1 Research Focus:dimensionality reduction· 1

Frequent Co-Authors

Anna Paola CarrieriiD· 3 shared Laura‐Jayne GardineriD· 2 shared Martyn WinniD· 2 shared Will P. M. RoweiD· 2 shared Alex ShawiD· 1 shared Andrew E. MayesiD· 1 shared Anthony J W HalliD· 1 shared Ashley MacGuire-Flanagan· 1 shared

Activity by Year

2019-2021 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 33 mutual co-author links

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3 shared papersGenomics

Anna Paola CarrieriORCID

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3 papers

Recent·Most cited

Interpreting machine learning models to investigate circadian regulation and facilitate exploration of clock function

Laura‐Jayne Gardiner, Rachel Rusholme‐Pilcher, Joshua Colmer, Hannah Rees, Juan Manuel Crescente, Anna Paola Carrieri, Susan Hanley Duncan, Edward O. Pyzer‐Knapp, Ritesh Krishna, Anthony J W Hall

This paper uses machine learning to predict circadian gene expression in Arabidopsis using DNA sequence features, identifies key regulatory elements, and optimizes sampling strategies for circadian transcript prediction.

Proceedings of the National Academy of Sciences · Aug 5, 2021Peer-reviewedGenomics

Explainable AI reveals changes in skin microbiome compositionlinked to phenotypic differences

Anna Paola Carrieri, Niina Haiminen, Sean Maudsley-Barton, Laura‐Jayne Gardiner, Barry Murphy, Andrew E. Mayes, Sarah Paterson, Sally G. Grimshaw, Martyn Winn, Cameron Shand, Panagiotis E. Hadjidoukas, Will P. M. Rowe, Stacy S. Hawkins, Ashley MacGuire-Flanagan, Jane Tazzioli, , Edward O. Pyzer‐Knapp

The paper develops an explainable AI approach to predict skin-related phenotypes from leg skin microbiome data and identifies microbial composition changes linked to these traits.

Scientific Reports · Feb 25, 2021Peer-reviewedBioinformatics

Streaming histogram sketching for rapid microbiome analytics

Will P. M. Rowe, Anna Paola Carrieri, Cristina Alcon-Giner, Shabhonam Caim, Alex Shaw, Kathleen Sim, John Simon Kroll, Lindsay J. Hall, Edward O. Pyzer‐Knapp, Martyn Winn

The paper introduces a method for rapid microbiome analytics using streaming histogram sketches, enabling efficient classification and similarity searches of microbiome data.

Microbiome · Mar 16, 2019Peer-reviewedGenomics