Author

D. E. Frankhouser

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· 2

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Assay:bulk RNA-seq· 2 Organism:mouse· 2 Approach:statistical model· 2 Research Focus:trajectory inference· 2 Research Focus:cell type annotation· 1

Frequent Co-Authors

D. O'Meally· 2 shared G. Marcucci· 2 shared L. Uechi· 2 shared R. C. Rockne· 2 shared S. Branciamore· 2 shared Y.-H. Kuo· 2 shared B. Zhang· 1 shared D. Zhao· 1 shared

Activity by Year

2021-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 42 mutual co-author links

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2 shared papersCancer Biology

D. O'Meally

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2 papers

Recent·Most cited

State-transition Modeling of Blood Transcriptome Predicts Disease Evolution and Treatment Response in Chronic Myeloid Leukemia

D. E. Frankhouser, R. C. Rockne, L. Uechi, D. Zhao, S. Branciamore, D. O'Meally, J. Irizarry, L. Ghoda, H. Ali, J. Trent, S. Forman, Y.-H. Fu, Y.-H. Kuo, B. Zhang, G. Marcucci

The paper uses transcriptome-based state-transition models to predict CML disease progression and treatment response using time-sequential blood samples from mice.

bioRxiv · Oct 11, 2023Cancer Biology

State-Transition Analysis of Time-Sequential microRNA Expression Predicts Development of Acute Myeloid Leukemia

D. E. Frankhouser, D. O'Meally, S. Branciamore, L. Uechi, H. Qin, X. Wu, N. Carlesso, G. Marcucci, Y.-H. Kuo, R. C. Rockne

This study uses time-sequential miRNA expression data from a mouse AML model to identify state transitions during leukemogenesis and shows the model predicts time to AML development.

bioRxiv · Mar 2, 2021 · PreprintCancer Biology