Dataset Information

Shin_2018_EGFR-PYK2-c-Met interaction network_model

ABSTRACT: Systems modelling of the EGFR-PYK2-c-Met interaction network predicted and prioritized synergistic drug combinations for Triple-negative breast cancer

SUBMITTER: Johannes Meyer

PROVIDER: BIOMD0000000826 | BioModels | 2024-09-02

REPOSITORIES: BioModels

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			Action	DRS
	BIOMD0000000826?filename=Shin2018-biopax2.owl	Owl
	BIOMD0000000826?filename=Shin2018-biopax3.owl	Owl
	BIOMD0000000826?filename=Shin2018-matlab.m	Other
	BIOMD0000000826?filename=Shin2018.cps	Other
	BIOMD0000000826?filename=Shin2018.ode	Other

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Systems modelling of the EGFR-PYK2-c-Met interaction network predicts and prioritizes synergistic drug combinations for triple-negative breast cancer.

Shin Sung-Young SY Müller Anna-Katharina AK Verma Nandini N Lev Sima S Nguyen Lan K LK

PLoS computational biology 20180619 6

Prediction of drug combinations that effectively target cancer cells is a critical challenge for cancer therapy, in particular for triple-negative breast cancer (TNBC), a highly aggressive breast cancer subtype with no effective targeted treatment. As signalling pathway networks critically control cancer cell behaviour, analysis of signalling network activity and crosstalk can help predict potent drug combinations and rational stratification of patients, thus bringing therapeutic and prognostic ...[more]

PMID: 29920512

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