Author

J. M. Ebos

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· 2

Signature Assays, Organisms & Methods

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Organism:mouse· 2 Approach:statistical model· 2 Approach:Bayesian inference· 1 Assay:bulk RNA-seq· 1 Research Focus:cell type annotation· 1 Research Focus:differential expression· 1 Research Focus:drug response / drug discovery· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

M. Mastri· 2 shared S. Benzekry· 2 shared A. Rodallec· 1 shared C. Nicolo· 1 shared C. Poignard· 1 shared C. Vaghi· 1 shared J. Ciccolini· 1 shared J. Mochel· 1 shared

Activity by Year

2019-2023 · peak 1/yr

Co-Author Collaboration Network

9 top collaborators · 30 mutual co-author links

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2 shared papersCancer Biology

M. Mastri

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2 papers

Recent·Most cited

Machine-learning and mechanistic modeling of primary and metastatic breast cancer growth after neoadjuvant targeted therapy

S. Benzekry, C. Nicolo, M. Mastri, J. M. Ebos

This paper develops a mathematical model and applies machine learning to predict neoadjuvant treatment response in breast cancer, finding limited predictive power of biomarkers for metastatic outcomes.

bioRxiv · Feb 22, 2023 · PreprintCancer Biology

A reduced Gompertz model for predicting tumor age using a population approach

C. Vaghi, A. Rodallec, R. Fanciullino, J. Ciccolini, J. Mochel, M. Mastri, C. Poignard, J. M. Ebos, S. Benzekry

The paper develops a reduced Gompertz model to predict tumor age using a population approach and Bayesian inference, showing improved accuracy and precision compared to traditional methods.

bioRxiv · Jun 25, 2019Cancer Biology