Author

Claire McQuin

Broad Institute

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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Bioinformatics· 4

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Assay:imaging· 4 Approach:deep learning· 3 Research Focus:benchmarking· 2 Research Focus:cell type annotation· 1 Research Focus:dimensionality reduction· 1 Organism:human· 1 Approach:machine learning (classical)· 1 Approach:statistical model· 1

Frequent Co-Authors

Allen GoodmaniD· 4 shared Anne Elizabeth CarpenteriD· 4 shared Shantanu SinghiD· 4 shared Juan Carlos CaicedoiD· 3 shared Aline Roch· 1 shared Anne Wilson· 1 shared Beth A. CiminiiD· 1 shared Csaba MolnáriD· 1 shared

Activity by Year

2018-2024 · peak 1/yr

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10 top collaborators · 32 mutual co-author links

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4 shared papersBioinformatics

Allen GoodmanORCID

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4 papers

Recent·Most cited

Learning representations for image-based profiling of perturbations

Nikita Moshkov, Michael Bornholdt, Santiago Benoit, Matthew Smith, Claire McQuin, Allen Goodman, Rebecca A. Senft, Yu Han, Mehrtash Babadi, Péter Horváth, Beth A. Cimini, Anne Elizabeth Carpenter, Shantanu Singh, Juan Carlos Caicedo

The paper develops a weakly supervised learning method to learn treatment effect representations from imaging data, improving downstream analysis performance.

Nature Communications · Feb 21, 2024Peer-reviewedBioinformatics

Label-free assessment of red blood cell storage lesions by deep learning

Minh Doan, Joseph A. Sebastian, Juan Carlos Caicedo, Stefanie Siegert, Aline Roch, Tracey R. Turner, Olga O. Mykhailova, Ruben N. Pinto, Claire McQuin, Allen Goodman, Michael J. Parsons, Olaf Wolkenhauer A, Holger Hennig, Shantanu Singh, Anne Wilson, , Anne Elizabeth Carpenter

This study uses deep learning and label-free imaging flow cytometry to classify red blood cell morphologies during storage, achieving 90% accuracy and predicting expiration dates based on morphological changes.

Proceedings of the National Academy of Sciences · Aug 24, 2020Peer-reviewedBioinformatics

Evaluation of Deep Learning Strategies for Nucleus Segmentation in Fluorescence Images

Juan C. Caicedo, Jonathan Roth, Allen Goodman, Tim Becker, Kyle W. Karhohs, Matthieu Broisin, Csaba Molnár, Claire McQuin, Shantanu Singh, Fabian Joachim Theis, Anne Elizabeth Carpenter

This study evaluates deep learning strategies for nucleus segmentation in fluorescence images, showing U-Net outperforms classical methods and reduces biologically relevant errors.

Cytometry Part A · Jul 16, 2019Peer-reviewedBioinformatics

Weakly supervised learning of single-cell feature embeddings

Juan Carlos Caicedo, Claire McQuin, Allen Goodman, Shantanu Singh, Anne Elizabeth Carpenter

This paper develops a weakly supervised CNN approach for learning single-cell feature embeddings from microscopy images, achieving better accuracy than classical features and competitiveness with transfer learning methods.

IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · Jun 1, 2018Peer-reviewedBioinformatics