Author

Z. Armstrong

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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Biochemistry· 1 Cell Biology· 1 Microbiology· 1

Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 2 Research Focus:differential expression· 2 Organism:human· 2 Assay:imaging· 2 Approach:machine learning (classical)· 2 Organism:bacteria (other)· 1 Research Focus:cell-cell communication· 1 Research Focus:clustering· 1

Frequent Co-Authors

J. Hazard· 2 shared M. Fan· 2 shared A. Alhifthi· 1 shared A. Daigavane· 1 shared A. Duren-Lubanski· 1 shared A. Geraschenko· 1 shared A. Narayanaswamy· 1 shared B. Fischbacher· 1 shared

Activity by Year

2019-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 36 mutual co-author links

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2 shared papersCell Biology

J. Hazard

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Molecular basis of sulfolactate synthesis by sulfolactaldehyde dehydrogenase from Rhizobium leguminosarum

J. Li, M. Sharma, R. W. Meek, A. Alhifthi, Z. Armstrong, N. Madiedo Soler, M. Lee, E. D. Goddard-Borger, J. N. Blaza, G. J. Davies, S. J. Williams

This paper reports the cryo-EM structure and kinetics of sulfolactaldehyde dehydrogenase from Rhizobium leguminosarum, revealing its cofactor usage and catalytic mechanism in sulfolactate synthesis.

bioRxiv · Mar 13, 2023 · PreprintBiochemistry

Integrating deep learning and unbiased automated high-content screening to identify complex disease signatures in human fibroblasts

L. Schiff, B. Migliori, Y. Chen, D. Carter, C. Bonilla, J. Hall, M. Fan, E. Tam, S. Ahadi, B. Fischbacher, A. Geraschenko, C. J. Hunter, S. Venugopalan, S. DesMarteau, A. Narayanaswamy, , Yang

This study develops a platform combining deep learning and high-content imaging to identify disease-specific phenotypes in Parkinson's fibroblasts, achieving high accuracy in distinguishing disease subtypes.

bioRxiv · Nov 13, 2020Cell Biology

A machine learning approach to define antimalarial drug action from heterogeneous cell-based screens

G. W. Ashdown, M. Dimon, M. Fan, F. S.-R. Teran, K. Witmer, D. C. A. Gaboriau, Z. Armstrong, J. Hazard, D. M. Ando, J. Baum

The paper develops a semi-supervised machine learning method to classify drug-induced morphological changes in malaria parasite cultures, enabling clustering of drugs by mechanism of action.

bioRxiv · Dec 19, 2019Microbiology