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Improving the efficacy of anti-cancer nanoparticles with data-driven mathematical modeling

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Life Sciences [q-bio] Cancer Computer Science [cs] Modeling and Simulation Life Sciences [q-bio] Life Sciences [q-bio] Pharmaceutical sciences Life Sciences [q-bio] Bioengineering Engineering Sciences [physics] Engineering Sciences [physics] Micro and nanotechnologies/Microelectronics Statistics [stat] Applications [stat.AP]
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https://hal.inria.fr/hal-01968959

Submitted on : Thursday, January 3, 2019-3:14:01 PM

Last modification on : Friday, January 21, 2022-3:10:07 AM

Long-term archiving on: Thursday, April 4, 2019-3:02:59 PM

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hal-01968959 , version 1 (03-01-2019)

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  • HAL Id : hal-01968959 , version 1

Cite

Cristina Vaghi, Anne Rodallec, Raphaelle Fanciullino, Joseph Ciccolini, Clair Poignard, et al.. Improving the efficacy of anti-cancer nanoparticles with data-driven mathematical modeling. Data Science Summer School, Jun 2018, Palaiseau, France. ⟨hal-01968959⟩

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