Author

Diego Luna

Johns Hopkins University Applied Physics LaboratoryPrimary ORCID profile (0009-0001-4658-0851 · 1 paper)Unclaimed ORCID profile · follow to get notified of new preprints

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

Same name, identified by ORCID

Diego LunaiD1 paper · Johns Hopkins University

Biology Disciplines

Click to filter author papers

Bioinformatics· 1 Neuroscience· 1

Signature Assays, Organisms & Methods

Click to filter · or open tag

Approach:deep learning· 1 Research Focus:dimensionality reduction· 1 Research Focus:drug response / drug discovery· 1 Assay:electronic health records· 1 Organism:human· 1 Assay:imaging· 1 Approach:statistical model· 1

Frequent Co-Authors

Erik C. JohnsoniD· 2 shared Alex Proescher· 1 shared Dowlette‐Mary Alam El DiniD· 1 shared Laura J. DunphyiD· 1 shared Lena SmirnovaiD· 1 shared Matthew J. RoosiD· 1 shared Ryan J. McQuilleniD· 1 shared Thomas HärtungiD· 1 shared

Activity by Year

2024-2025 · peak 1/yr

Co-Author Collaboration Network

8 top collaborators · 18 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 papersNeuroscience

Erik C. JohnsonORCID

Mutual co-authors in cluster

Author profile →

2 papers

Recent·Most cited

Modeling Organoid Population Electrophysiology Dynamics

Matthew J. Roos, Diego Luna, Dowlette‐Mary Alam El Din, Thomas Härtung, Lena Smirnova, Alex Proescher, Erik C. Johnson

This paper adapts CEBRA to generate low-dimensional embeddings of neural organoid electrophysiology data for analyzing activity changes and clustering bursting phenomena.

bioRxiv · Mar 2, 2025 · PreprintNeuroscience

DeepPaint: A deep-learning package for Cell Painting Image Classification

Diego Luna, Erik C. Johnson, Laura J. Dunphy, Ryan J. McQuillen

DeepPaint is a deep learning package that classifies Cell Painting images without requiring cell segmentation, achieving high accuracy in binary and multiclass classification of biological perturbations.

bioRxiv · Oct 8, 2024 · PreprintBioinformatics