Data-driven selection and parameter estimation for DNA methylation mathematical models

DOI10.1016/j.jtbi.2019.01.012zbMath1409.92085OpenAlexW2910571328WikidataQ91003158 ScholiaQ91003158MaRDI QIDQ1730118

Nikos I. Kavallaris, Anastasios Matzavinos, Mark Mc Auley, Loukas Zagkos, Karen Larson, J. A. Roberts

Publication date: 11 March 2019

Published in: Journal of Theoretical Biology (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/10034/621823

zbMATH Keywords

parameter estimationmodel selectionDNA methylationCpG dyadsgene promoter

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Biochemistry, molecular biology (92C40) Software, source code, etc. for problems pertaining to biology (92-04)

Related Items (1)

A Bayesian framework to estimate fluid and material parameters in micro-swimmer models

Uses Software

• Pi4U

Cites Work

• A dynamic multi-compartmental model of DNA methylation with demonstrable predictive value in hematological malignancies
• \(\Pi\)4U: a high performance computing framework for Bayesian uncertainty quantification of complex models
• Mathematical models of DNA methylation dynamics: implications for health and ageing
• Bayesian Reasoning and Machine Learning

This page was built for publication: Data-driven selection and parameter estimation for DNA methylation mathematical models