Author

Kerrie L. Marie

National Cancer InstituteORCID 0000-0002-2860-918XUnclaimed ORCID profile · follow to get notified of new preprints

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Assay:bulk RNA-seq· 2 Research Focus:cell type annotation· 2 Organism:human· 2 Approach:statistical model· 2 Research Focus:clustering· 1 Research Focus:differential expression· 1 Research Focus:gene regulatory network inference· 1 Organism:mouse· 1

Frequent Co-Authors

Chi-Ping DayiD· 2 shared Erfan Sadeqi AzeriD· 2 shared Eva Pérez‐GuijarroiD· 2 shared Farid Rashidi MehrabadiiD· 2 shared Funda Ergün· 2 shared Glenn T MerlinoiD· 2 shared Maxwell P. LeeiD· 2 shared S. Cenk ŞahinalpiD· 2 shared

Activity by Year

2020-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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2 shared papersBioinformatics

Chi-Ping DayORCID

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3 papers

Recent·Most cited

Travelling Waves in Gene Expression: A Mathematical Model of Cell-State Dynamics in Melanoma

Charlotte E Taylor Barca, Rotem Leshem, Vishaka Gopalan, Sarah Woolner, Kerrie L. Marie, Gareth Wyn Jones, Oliver E. Jensen

The paper develops a mathematical model to study gene-expression dynamics and cell-state switching in melanoma using deterministic differential equations and cooperative binding approximations.

Bulletin of Mathematical Biology · Jun 8, 2026Peer-reviewedCancer Biology

Profiles of expressed mutations in single cells reveal subclonal expansion patterns and therapeutic impact of intratumor heterogeneity

Farid Rashidi Mehrabadi, Kerrie L. Marie, Eva Pérez‐Guijarro, Salem Malikić, Erfan Sadeqi Azer, Howard H. Yang, Can Kızılkale, Charli Gruen, Welles Robinson, Huaitian Liu, Michael C. Kelly, Christina Marcelus, Sandra Sczerba Burkett, Aydın Buluç, Funda Ergün, , S. Cenk Şahinalp

Trisicell infers subclonal expansion patterns and therapeutic impact of intratumor heterogeneity from scRNAseq data, revealing correlations between mutational composition and tumor aggressiveness.

bioRxiv · Mar 26, 2021 · PreprintBioinformatics

PhISCS-BnB: A Fast Branch and Bound Algorithm for the Perfect Tumor Phylogeny Reconstruction Problem

Erfan Sadeqi Azer, Farid Rashidi Mehrabadi, Salem Malikić, Xuan Cindy Li, Osnat Bartok, Kevin Litchfield, Ronen Levy, Yardena Samuels, Alejandro A. Schäffer, Edward Michael Gertz, Chi‐Ping Day, Eva Pérez‐Guijarro, Kerrie L. Marie, Maxwell P. Lee, Glenn T Merlino, , S. Cenk Şahinalp

PhISCS-BnB computes the most likely perfect tumor phylogeny from single-cell sequencing data, offering optimality guarantees and significant speed improvements over existing methods.

Bioinformatics · Jul 1, 2020Peer-reviewedBioinformatics