Author

Danny Salem

National Research Council CanadaORCID 0000-0002-2689-2997Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Research Focus:dimensionality reduction· 1 Assay:imaging· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Miroslava Čuperlović‐CulfiD· 2 shared Anuradha SurendraiD· 1 shared Graeme S. V. McDowell· 1 shared Hilary Phenix· 1 shared Mads KærniD· 1 shared Pengcheng XiiD· 1 shared Yifeng LiiD· 1 shared

Activity by Year

2021-2024 · peak 1/yr

Co-Author Collaboration Network

7 top collaborators · 13 mutual co-author links

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2 shared papersBioinformatics

Miroslava Čuperlović‐CulfORCID

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2 papers

Recent·Most cited

Projection Statistics ProST Online statistical assessment of group separation in data projection analysis

Danny Salem, Anuradha Surendra, Graeme S. V. McDowell, Miroslava Čuperlović‐Culf

ProST provides a user-friendly tool for assessing the statistical significance of group separation in data projection analysis using PCA, t-SNE, UMAP, and LDA.

bioRxiv · Sep 4, 2024 · PreprintBioinformatics

YeastNet: Deep Learning Enabled Accurate Segmentation of Budding Yeast Cells in Bright-field Microscopy

Danny Salem, Yifeng Li, Pengcheng Xi, Hilary Phenix, Miroslava Čuperlović‐Culf, Mads Kærn

YeastNet improves yeast cell segmentation in bright-field microscopy using a U-Net model, outperforming existing tools and providing a Python package for automated analysis.

Applied Sciences · Mar 17, 2021Peer-reviewedBioinformatics