Cargando…
Protocol for stratification of triple-negative breast cancer patients using in silico signaling dynamics
Personalized kinetic models can predict potential biomarkers and drug targets. Here, we provide a step-by-step approach for building an executable mathematical model from text and integrating transcriptomic datasets. We additionally describe the steps to personalize the mechanistic model and to stra...
Autores principales: | Imoto, Hiroaki, Yamashiro, Sawa, Murakami, Ken, Okada, Mariko |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9389415/ https://www.ncbi.nlm.nih.gov/pubmed/35990741 http://dx.doi.org/10.1016/j.xpro.2022.101619 |
Ejemplares similares
-
A text-based computational framework for patient -specific modeling for classification of cancers
por: Imoto, Hiroaki, et al.
Publicado: (2022) -
A Computational Framework for Prediction and Analysis of Cancer Signaling Dynamics from RNA Sequencing Data—Application to the ErbB Receptor Signaling Pathway
por: Imoto, Hiroaki, et al.
Publicado: (2020) -
Molecular stratification within triple-negative breast cancer subtypes
por: Wang, Dong-Yu, et al.
Publicado: (2019) -
Association between mesothelin expression and survival outcomes in patients with triple-negative breast cancer: a protocol for a systematic review
por: Wang, Mei, et al.
Publicado: (2016) -
Stratification of Prognosis of Triple-Negative Breast Cancer Patients Using Combinatorial Biomarkers
por: Yue, Yong, et al.
Publicado: (2016)