Author

B. Fischbacher

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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Cell Biology· 2

Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 2 Approach:deep learning· 2 Organism:human· 2 Assay:imaging· 2 Research Focus:cell-cell communication· 1 Research Focus:clustering· 1 Research Focus:differential expression· 1 Assay:fluorescence / live-cell microscopy· 1

Frequent Co-Authors

D. Paull· 2 shared A. Daigavane· 1 shared A. Duren-Lubanski· 1 shared A. Geraschenko· 1 shared A. Narayanaswamy· 1 shared B. J. Hartley· 1 shared B. Migliori· 1 shared B. Williams· 1 shared

Activity by Year

2020-2020 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 37 mutual co-author links

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2 shared papersCell Biology

D. Paull

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Author profile →

2 papers

Recent·Most cited

Modular deep learning enables automated identification of monoclonal cell lines

B. Fischbacher, S. Hedaya, B. J. Hartley, Z. Wang, G. Lallos, D. Hutson, M. Zimmer, J. Brammer, The NYSCF Global Stem Cell Array Team, D. Paull

This paper introduces Monoqlo, a deep learning workflow that automatically detects colony presence and identifies clonality from cellular imaging, enabling scalable and standardized monoclonalization.

bioRxiv · Dec 28, 2020Cell Biology

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