Author

M. Berndl

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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Bioengineering· 1 Cell Biology· 1

Signature Assays, Organisms & Methods

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Approach:machine learning (classical)· 2 Research Focus:cell type annotation· 1 Research Focus:cell-cell communication· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Research Focus:drug response / drug discovery· 1 Assay:fluorescence / live-cell microscopy· 1 Organism:human· 1

Frequent Co-Authors

M. Frumkin· 2 shared A. Daigavane· 1 shared A. Duren-Lubanski· 1 shared A. Geraschenko· 1 shared A. Narayanaswamy· 1 shared B. Fischbacher· 1 shared B. Migliori· 1 shared B. Williams· 1 shared

Activity by Year

2020-2021 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 37 mutual co-author links

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2 shared papersBioengineering

M. Frumkin

Mutual co-authors in cluster

Author profile →

2 papers

Recent·Most cited

Machine Learning Optimization of Photosynthetic Microbe Cultivation and Recombinant Protein Production

C. Gamble, D. Bryant, D. Carrieri, E. Bixby, J. Dang, J. Marshall, D. Doughty, L. Colwell, M. Berndl, J. Roberts, M. Frumkin

This study uses machine learning to optimize culture conditions for producing therapeutic proteins in spirulina, doubling production and improving volumetric productivity by 70-100%.

bioRxiv · Aug 6, 2021 · PreprintBioengineering

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