Author

L. A. D. Cooper

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

Biology Disciplines

Click to filter author papers

Pathology· 3 Cancer Biology· 1

Signature Assays, Organisms & Methods

Click to filter · or open tag

Approach:deep learning· 4 Research Focus:cell type annotation· 3 Organism:human· 3 Assay:imaging· 2 Research Focus:clustering· 1 Research Focus:differential expression· 1 Research Focus:drug response / drug discovery· 1 Assay:flow cytometry· 1

Frequent Co-Authors

C. Tang· 2 shared D. L. Jaye· 2 shared J. E. Lewis· 2 shared A. A. Ivanov· 1 shared A. Aljudi· 1 shared A. Khanna· 1 shared A. Wahafu· 1 shared B. R. Drumheller· 1 shared

Activity by Year

2021-2024 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 20 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

2 shared papersPathology

C. Tang

Mutual co-authors in cluster

Author profile →

4 papers

Recent·Most cited

Automated Deep Learning-Based Diagnosis and Molecular Characterization of Acute Myeloid Leukemia using Flow Cytometry

J. E. Lewis, L. A. D. Cooper, D. L. Jaye, O. Pozdnyakova

This paper develops a deep learning pipeline for automated AML diagnosis and molecular characterization using flow cytometry data, achieving high accuracy in classification and variant prediction.

Modern Pathology · Jan 1, 2024Peer-reviewedPathology

A Deep Learning Approach for Histology-Based Nuclei Segmentation and Tumor Microenvironment Characterization

R. Rong, H. Sheng, K. W. Jin, F. Wu, D. Luo, Z. Wen, C. Tang, D. M. Yang, L. Jia, M. Amgad, L. A. D. Cooper, Y. Xie, X. Zhan, S. Wang, G. Xiao

This study introduces HD-Yolo, a deep learning method for faster and more accurate nuclei segmentation and tumor microenvironment quantification in whole-slide images.

bioRxiv · Dec 8, 2022 · PreprintPathology

An Automated Pipeline for Differential Cell Counts on Whole-Slide Bone Marrow Aspirate Smears

J. E. Lewis, C. W. Shebelut, B. R. Drumheller, X. Zhang, N. Shanmugam, M. Attieh, M. C. Horwath, A. Khanna, G. H. Smith, D. A. Gutman, A. Aljudi, L. A. D. Cooper, D. L. Jaye

This paper develops an automated machine learning pipeline for differential cell counts on whole-slide bone marrow aspirate smears, showing high concordance with manual counts and reduced intra-slide variance.

bioRxiv · May 26, 2022Pathology

Systematic discovery of mutation-directed neo-protein-protein interactions in cancer

X. Mo, Q. Niu, A. A. Ivanov, Y. H. Tsang, C. Tang, C. Shu, A. Wahafu, S. Doyle, D. Cicka, X. Yang, D. Fan, M. A. Reyna, L. A. D. Cooper, C. S. Moreno, W. Zhou, , H. Fu

The paper identifies mutation-directed neo-protein-protein interactions in cancer using a high-throughput screening platform, revealing therapeutic opportunities through neo-interaction rewiring.

bioRxiv · Oct 3, 2021Cancer Biology