Author

Giang Ong

Nanostring Technologies (United States)

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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Cancer Biology· 1 Genomics· 1

Signature Assays, Organisms & Methods

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Research Focus:differential expression· 2 Assay:spatial transcriptomics· 2 Research Focus:cell type annotation· 1 Research Focus:cell-cell communication· 1 Approach:deep learning· 1 Organism:human· 1 Approach:machine learning (classical)· 1 Assay:proteomics· 1

Frequent Co-Authors

Ayodele J. OmotosoiD· 1 shared Bassem Ben Cheikh· 1 shared Carmen Gómez· 1 shared Claire WilliamsiD· 1 shared Destiny B. BurnettiD· 1 shared Felipe Segato DezemiD· 1 shared Jasmine PlummeriD· 1 shared Joseph BeechemiD· 1 shared

Activity by Year

2023-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

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1 shared paperGenomics

Ayodele J. OmotosoORCID

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Author profile →

2 papers

Recent·Most cited

Breast cancer through the lens of whole transcriptome spatial imaging

Claire Williams, Yi Cui, Michael J. Patrick, Giang Ong, Terence C. Theisen, Megan Vandenberg, Joseph Beechem, Patrick J. Danaher

The paper uses spatial transcriptomics to analyze a breast tumor, identifying spatially distinct biological processes and potential therapeutic targets.

Scientific Reports · Sep 9, 2026Peer-reviewedCancer Biology

A Machine Learning One-Class Logistic Regression Model to Predict Stemness in Single Cell Transcriptomics and Spatial Omics Datasets

Felipe Segato Dezem, Maycon Marção, Bassem Ben Cheikh, Nadya Nikulina, Ayodele J. Omotoso, Destiny B. Burnett, Priscila Campos dos Santos Coelho, Simone Badal, Judith Hurley, Carmen Gómez, Tien Phan-Everson, Giang Ong, Luciano Gastón Martelotto, Zachary R. Lewis, Sophia George, , Jasmine Plummer

The paper applies a one-class logistic regression model to predict stemness in single-cell and spatial omics datasets without requiring cell annotation.

BMC Genomics · Nov 28, 2023Peer-reviewedGenomics