Author

Pavithra Kumar

Johns Hopkins University

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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Cancer Biology· 1

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Research Focus:benchmarking· 1 Assay:bulk RNA-seq· 1 Research Focus:cell type annotation· 1 Research Focus:differential expression· 1 Research Focus:gene regulatory network inference· 1 Organism:human· 1 Approach:machine learning (classical)· 1 Organism:mouse· 1

Frequent Co-Authors

Bradley IsaacsiD· 1 shared Da PengiD· 1 shared Edroaldo Lummertz da RochaiD· 1 shared Franklin W. HuangiD· 1 shared Kathleen T. DiNapoliiD· 1 shared Patrick CahaniD· 1 shared Qin BianiD· 1 shared Rachel GleyzeriD· 1 shared

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2021-2021 · peak 1/yr

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10 top collaborators · 45 mutual co-author links

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1 shared paperCancer Biology

Bradley IsaacsORCID

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1 paper

Recent·Most cited

Evaluating the transcriptional fidelity of cancer models

Da Peng, Rachel Gleyzer, Wen-Hsin Tai, Pavithra Kumar, Qin Bian, Bradley Isaacs, Edroaldo Lummertz da Rocha, Stephanie Cai, Kathleen T. DiNapoli, Franklin W. Huang, Patrick Cahan

The paper evaluates the transcriptional fidelity of cancer models using a machine learning tool called CancerCellNet, finding that genetically engineered mice and tumoroids generally show higher fidelity than other model types.

Genome Medicine · Apr 29, 2021Peer-reviewedCancer Biology