16 papers · ranked by Valyu relevance
Gabriele Lillacci, Mustafa Khammash, Anand R. Asthagiri
A central challenge in computational modeling of biological systems is the determination of the model parameters. Typically, only a fraction of the parameters (such as kinetic rate constants) are experimentally measured, while the rest are often fitted. The fitting process is usually based on experimental time course…
Peng Wang, Ge Li, Yong Peng, Rusheng Ju
Parameter estimation is one of the key technologies for system identification. The Bayesian parameter estimation algorithms are very important for identifying stochastic systems. In this paper, a random finite set based algorithm is proposed to overcome the disadvantages of the existing Bayesian parameter estimation…
Stephen B. Broomell, Sabina J. Sloman, Lisheng He
Behavioral models are instrumental for studying human cognition, yet many inferences derived from such models fail to generalize. We argue that this is driven in part by the increasing complexity of behavioral models, where non-linearities and discontinuities create dynamic parameter interactions that limit the…
Laura Marie Helleckes, Michael Osthege, Wolfgang Wiechert, Eric von Lieres + 2 more
'Eric von Lieres' 'Marco Oldiges' 'Dina Schneidman-Duhovny'] High-throughput experimentation has revolutionized data-driven experimental sciences and opened the door to the application of machine learning techniques. Nevertheless, the quality of any data analysis strongly depends on the quality of the data and…
Fuad S. Alduais, Neveen Sayed-Ahmed
The most essential process in statistical image and signal processing is the parameter estimation of probability density functions (PDFs). The estimation of the probability density functions is a contentious issue in the domains of artificial intelligence and machine learning. The study examines challenges related to…
Katerina Tashkova, Peter Korošec, Jurij Šilc, Ljupčo Todorovski + 1 more
'Sašo Džeroski'] Background We address the task of parameter estimation in models of the dynamics of biological systems based on ordinary differential equations (ODEs) from measured data, where the models are typically non-linear and have many parameters, the measurements are imperfect due to noise, and the studied…
Maria Rodriguez-Fernandez, Jose A Egea, Julio R Banga
Background We consider the problem of parameter estimation (model calibration) in nonlinear dynamic models of biological systems. Due to the frequent ill-conditioning and multi-modality of many of these problems, traditional local methods usually fail (unless initialized with very good guesses of the parameter vector).…
Attila Gábor, Julio R. Banga
Background Dynamic modelling provides a systematic framework to understand function in biological systems. Parameter estimation in nonlinear dynamic models remains a very challenging inverse problem due to its nonconvexity and ill-conditioning. Associated issues like overfitting and local solutions are usually not…
Timothy E. O’Brien, Jack W. Silcox
Use of nonlinear statistical methods and models are ubiquitous in scientific research. However, these methods may not be fully understood, and as demonstrated here, commonly-reported parameter p-values and confidence intervals may be inaccurate. The gentle introduction to nonlinear regression modelling and…
Jesús Miguel Zamudio Lara, Laurent Dewasme, Héctor Hernández Escoto, Alain Vande Wouwer + 3 more
'Alain Vande Wouwer' 'Jose A. Egea' 'Carlos Vilas' 'Míriam R. García'] In this study, two dynamic models of beer fermentation are proposed, and their parameters are estimated using experimental data collected during several batch experiments initiated with different sugar concentrations. Biomass, sugar, ethanol, and…
Akatsuki Kimura, Antonio Celani, Hiromichi Nagao, Timothy Stasevich + 1 more
'Kazuyuki Nakamura'] Construction of quantitative models is a primary goal of quantitative biology, which aims to understand cellular and organismal phenomena in a quantitative manner. In this article, we introduce optimization procedures to search for parameters in a quantitative model that can reproduce experimental…
Omid Ghasemi, Merry L Lindsey, Tianyi Yang, Nguyen Nguyen + 2 more
'Yufei Huang' 'Yu-Fang Jin'] Background The availability of temporal measurements on biological experiments has significantly promoted research areas in systems biology. To gain insight into the interaction and regulation of biological systems, mathematical frameworks such as ordinary differential equations have been…
Qianqian Tong, Zhiyong Yuan, Mianlun Zheng, Xiangyun Liao + 2 more
'Weixu Zhu' 'Guian Zhang'] The elastic parameters of soft tissues are important for medical diagnosis and virtual surgery simulation. In this study, we propose a novel nonlinear parameter estimation method for soft tissues. Firstly, an in-house data acquisition platform was used to obtain external forces and their…
Hong Qin
Parameter identification is an important branch of automatic control. Due to its special function, it has been widely used in various fields, especially the modeling of complex systems or systems whose parameters are not easy to determine. With the development of control technology, the scale of the control object is…
Michael Sinsbeck, Marvin Höge, Wolfgang Nowak
Methods for sequential design of computer experiments typically consist of two phases. In the first phase, the exploratory phase, a space-filling initial design is used to estimate hyperparameters of a Gaussian process emulator (GPE) and to provide some initial global exploration of the model function. In the second…
Zahra Amini Farsani, Volker J. Schmid, Udo Von Toussaint
Background: For the kinetic models used in contrast-based medical imaging, the assignment of the arterial input function named AIF is essential for the estimation of the physiological parameters of the tissue via solving an optimization problem. Objective: In the current study, we estimate the AIF relayed on the…