output
20082024
most citedFederated Learning Enables Big Data for Rare Cancer Boundary Detection

390 citations

Showing 2013Show all

5 papers · 1 filter

q-bio.QM2013

A High-Performance Cellular Automaton Model of Tumor Growth with Dynamically Growing Domains

Jan Poleszczuk, Heiko Enderling

Tumor growth from a single transformed cancer cell up to a clinically apparent mass spans many spatial and temporal orders of magnitude. Implementation of cellular automata simulat…

q-bio.PE2013

Edge effects in game theoretic dynamics of spatially structured tumours

Artem Kaznatcheev, Jacob G. Scott, David Basanta

Background: Analysing tumour architecture for metastatic potential usually focuses on phenotypic differences due to cellular morphology or specific genetic mutations, but often ign…

q-bio.TO2013

Mathematical modeling of the metastatic process

Jacob G. Scott, Philip Gerlee, David Basanta +3

Mathematical modeling in cancer has been growing in popularity and impact since its inception in 1932. The first theoretical mathematical modeling in cancer research was focused on…

q-bio.TO2013★ 1 cited

Exploiting ecological principles to better understand cancer progression and treatment

David Basanta, Alexander R. A. Anderson

A small but growing number of people are finding interesting parallels between ecosystems as studied by ecologists (think of a Savanna or the Amazon rain forest or a Coral reef) an…

q-bio.PE2013

Evolution of intratumoral phenotypic heterogeneity: the role of trait inheritance

Jill Gallaher, Alexander R. A. Anderson

A tumor can be thought of as an ecosystem, which critically means that we cannot just consider it as a collection of mutated cells but more as a complex system of many interacting…