Author

A. Kirillova

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

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Cell Biology· 1

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Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Approach:deep learning· 1 Assay:fluorescence / live-cell microscopy· 1 Organism:mouse· 1

Frequent Co-Authors

A. Chang· 1 shared A. T. V. Ho· 1 shared C. A. Holbrook· 1 shared C. M. Madl· 1 shared H. Blau· 1 shared K. E. G. Magnusson· 1 shared M. Togninalli· 1 shared Y. X. Wang· 1 shared

Activity by Year

2022-2022 · peak 1/yr

Co-Author Collaboration Network

8 top collaborators · 28 mutual co-author links

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1 shared paperCell Biology

A. Chang

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1 paper

Recent·Most cited

Machine learning-based classification of binary dynamic fluorescence signals reveals muscle stem cell fate transitions in response to pro-regenerative niche factors

M. Togninalli, A. T. V. Ho, C. M. Madl, C. A. Holbrook, Y. X. Wang, K. E. G. Magnusson, A. Kirillova, A. Chang, H. Blau

This study uses machine learning to analyze dynamic fluorescence signals and classify muscle stem cell fate transitions in response to niche factors like PGE2 and OSM.

bioRxiv · Jun 17, 2022 · PreprintCell Biology