A text-based computational framework for patient -specific modeling for classification of cancers
Hiroaki Imoto, Sawa Yamashiro, Mariko Okada
Abstract
simulation of 377 patients with breast cancer using The Cancer Genome Atlas (TCGA) transcriptome datasets. The temporal dynamics of Akt, extracellular signal-regulated kinase (ERK), and c-Myc in each patient were able to accurately predict the difference in prognosis and sensitivity to kinase inhibitors in triple-negative breast cancer (TNBC). Our model applies to any type of signaling network and facilitates the network-based use of prognostic markers and prediction of drug response.
Topics & Concepts
ExecutableIn silicoComputational biologySystems biologyErbBBreast cancerComputer scienceBioinformaticsCancerBiologyGeneGeneticsOperating systemBioinformatics and Genomic NetworksBiomedical Text Mining and OntologiesComputational Drug Discovery Methods