Improved mechanistic understanding of biochemical networks is one of the driving ambitions
of Systems Biology. Computational modeling allows the integration of various sources of …

PESTO is a widely applicable and highly customizable toolbox for parameter estimation in
MathWorks MATLAB. It offers scalable algorithms for optimization, uncertainty and …

Background In quantitative biology, mathematical models are used to describe and analyze
biological processes. The parameters of these models are usually unknown and need to be …

In this work we present results of a detailed Bayesian parameter estimation for an analysis of
ordinary differential equation models. These depend on many unknown parameters that …

In this work, we present the R package BASS as a tool for nonparametric regression. The
primary focus of the package is fitting fully Bayesian adaptive spline surface (BASS) models …

Inductive inference plays a central role in the study of biological systems where one aims to
increase their understanding of the system by reasoning backwards from uncertain …

Machine learning and computational intelligence are two methods for achieving this (CI);
traditional operational research methods are combined with machine learning-based …

Motivation Mathematical models have become standard tools for the investigation of cellular
processes and the unraveling of signal processing mechanisms. The parameters of these …

Partial differential equation (PDE) models are widely used in engineering and natural
sciences to describe spatio-temporal processes. The parameters of the considered …

The article is a semantic plagiarism of a paper that has already been published in Quality
and Reliability Engineering International, Volume 33, Issue 8 (2017), 2361-2383, doi …