Inria Scientific Researcher (Charge de Recherches)
Co-head (with J. Ciccolini) of the Inria-Inserm project-teamCOMPO: COMPutational pharmacology and clinical Oncology located in Marseille.
Inria Sophia-Antipolis
Center for Research on Cancer of Marseille
Inserm U1068, CNRS UMR7258, Institut Paoli-Calmettes
Pharmacy faculty, Aix-Marseille University
Research interests
My research is focused on applied mathematics and statistics to cancer research and developing methods for quantitative personalized oncology. Some keywords include- Computational oncology
- Mechanistic learning (= mechanistic modeling + machine learning)
- Pharmacometrics
- Programming for data science (R / python)
- Clinical applications of math/stat/ML modeling in oncology
- Metastasis
- Immuno-oncology
- Personalized oncology
Software (last update 2022)
Here is a sample list of software I wrote with links to gitlab/githup repos and complete Readmes.
nlml_onco
This software analyses multiple data arising from clinical oncology (routine care and clinical trials). This data can be of two types: 1) static (e.g., baseline features), from clinical, biological, molecular (e.g., transcriptomic or mutation data) 2) longitudinal (multiple time points per individual): tumor kinetics, biomarkers The second type is modeled using the framework of nonlinear mixed-effects modeling. All features are then analyzed using data science techniques (preprocess, feature selection, machine learning algorithms), in order to predict survival outcome.
stats_pioneer
This software was built to analyse the PIONeeR (Precision Immuno-Oncology for advanced Non-small cell lung cancer patients with PD-(L) 1 ICI Resistance) data. PIONeeR is a prospective, multicenter study with primary objective being to validate the existence of a hypothetical immune profile explaining resistance to immunotherapy in non-small cell lung cancer patients.
metamats_size
This software fits models of metastatic development to longitudinal data of metastatic sizes and provides simulation and visualization tools for metastatic modeling.
The full README is accessible here
metastats
This R package implements mixed-effects statistical estimation of parameters for a mechanistic model predicting time to metastatic relapse.
The full README is accessible here
mechanistic_predict_neuroblastoma
This python code generates the results and figures reported in the paper
Benzekry, S. et al., Development and Validation of
a Prediction Model of Overall Survival in High-Risk Neuroblastoma Using Mechanistic Modeling of Metastasis,
JCO: Clinical Cancer Informatics, 2020.
The full README and code are accessible here
metamats_burden
This Matlab software is a minimal code for fitting simultaneously primary tumor growth and metastatic burden data using nonlinear mixed-effects.
The full README is accessible here
metamats_core_matlab
This code is devoted to the simulation of a partial differential equation (PDE) based model for the time development of a population of secondary tumors (metastases).
The full README is accessible here
metamats_core_python
This code is devoted to the simulation of a partial differential equation (PDE) based model for the time development of a population of secondary tumors (metastases).
The full README is accessible here
plumky
The full README is accessible here
carcinom
A software for nonlinear regression of tumor growth models and statistical inference
The full README is accessible here
covid_radiomics_ML
Machine learning for radiomics features extracted from COVID-19 patient CT scan data
The full README is accessible here
Contact
Email
benzekry@phare.normalesup.org
Address
Pharmacy faculty, Aix-Marseille University
27, bd Jean Moulin
13005 Marseille
