Author

S. Branciamore

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· 5 Biophysics· 1 Immunology· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 6 Assay:bulk RNA-seq· 4 Organism:human· 3 Research Focus:trajectory inference· 3 Research Focus:cell signaling & trafficking· 2 Research Focus:cell type annotation· 2 Approach:deep learning· 2 Research Focus:differential expression· 2

Frequent Co-Authors

R. C. Rockne· 5 shared G. Marcucci· 4 shared Y.-H. Kuo· 4 shared D. Maestrini· 3 shared D. O'Meally· 3 shared N. Carlesso· 3 shared A. S. Rodin· 2 shared D. Abler· 2 shared

Activity by Year

2017-2023 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 33 mutual co-author links

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5 shared papersCancer Biology

R. C. Rockne

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Author profile →

7 papers

Recent·Most cited

Systems profiling reveals recurrently dysregulated cytokine signaling responses in ER+ breast cancer patients' blood

B. Orcutt-Jahns, J. R. Lima Junior, R. C. Rockne, A. Matache, S. Branciamore, E. Hung, A. S. Rodin, P. P. Lee, A. S. Meyer

The paper profiles cytokine signaling responses in breast cancer patients and finds coordinated dysregulation across pathways and cell types, suggesting immune cell reprogramming and immunosuppression.

bioRxiv · Oct 31, 2023Cancer Biology

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

Bayesian network models identify co-operative GPCR:G protein interactions that contribute to G protein coupling

E. Mukhaleva, N. Ma, W. J. C. Van Der Velden, G. Gogoshin, S. Branciamore, S. Bhattacharya, A. S. RODIN, N. Vaidehi

This study uses Bayesian network modeling to identify cooperative GPCR:G protein interactions and their allosteric effects, revealing subnetworks that contribute to G protein coupling selectivity.

bioRxiv · Oct 9, 2023Biophysics

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

Mathematical deconvolution of CAR T-cell proliferation and exhaustion from real-time killing assay data

P. Sahoo, X. Yang, D. Abler, D. Maestrini, V. Adhikarla, D. Frankhouser, H. Cho, V. Machuca, D. Wang, M. Barish, M. Gutova, S. Branciamore, C. E. Brown, R. C. Rockne

This paper uses a mathematical model to analyze CAR T-cell killing dynamics in glioma, showing dose-dependent effects on proliferation and exhaustion.

bioRxiv · Nov 9, 2019Immunology

State-Transition Analysis of Time-Sequential Gene Expression Identifies Critical Points That Predict Leukemia Development

R. C. Rockne, S. Branciamore, J. Qi, D. Frankhouser, D. O'Meally, W.-K. Hua, G. J. Cook, L. Zhang, E. Carnahan, A. Marom, H. Wu, D. Maestrini, X. Wu, Y.-C. Yuan, Z. Liu, , G. Marcucci

The paper models leukemia transcriptome dynamics in a state-space to identify critical points that predict disease development.

bioRxiv · Jun 4, 2019 · PreprintCancer Biology

Aging in a relativistic biological space-time

D. Maestrini, D. Abler, V. Adhikarla, S. Armenian, S. Branciamore, N. Carlesso, G. Marcucci, Y.-H. Kuo, P. Sahoo, R. Rockne

This paper proposes a theoretical framework modeling biological aging as trajectories in a mathematical space-time, with applications to cancer and aging rates.

bioRxiv · Dec 5, 2017Cancer Biology