A text-based computational framework for patient -specific modeling for classification of cancers.

ABSTRACT: Patient heterogeneity precludes cancer treatment and drug development; hence, development of methods for finding prognostic markers for individual treatment is urgently required. Here, we present Pasmopy (Patient-Specific Modeling in Python), a computational framework for stratification of patients using in silico signaling dynamics. Pasmopy converts texts and sentences on biochemical systems into an executable mathematical model. Using this framework, we built a model of the ErbB receptor signaling network, trained in cultured cell lines, and performed in silico 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 abl

SUBMITTER: Imoto H 

PROVIDER: S-EPMC9076893 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature