From data to QSP models: a pipeline for using Boolean networks for hypothesis inference and dynamic model building

2022 ◽  
Author(s):  
M. Putnins ◽  
O. Campagne ◽  
D. E. Mager ◽  
I. P. Androulakis
Keyword(s):  
Dynamic Model ◽  
Model Building ◽  
Boolean Networks

scholarly journals Dynamic model updating (DMU) approach for statistical learning model building with missing data

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Rahi Jain ◽  
Wei Xu
Keyword(s):  
Missing Data ◽  
Dynamic Model ◽  
Statistical Models ◽  
Missing Values ◽  
Model Building ◽  
Model Updating ◽  
Biological Data ◽  
Bayesian Regression ◽  
Biological Research ◽  
Original Dataset

Abstract Background Developing statistical and machine learning methods on studies with missing information is a ubiquitous challenge in real-world biological research. The strategy in literature relies on either removing the samples with missing values like complete case analysis (CCA) or imputing the information in the samples with missing values like predictive mean matching (PMM) such as MICE. Some limitations of these strategies are information loss and closeness of the imputed values with the missing values. Further, in scenarios with piecemeal medical data, these strategies have to wait to complete the data collection process to provide a complete dataset for statistical models. Method and results This study proposes a dynamic model updating (DMU) approach, a different strategy to develop statistical models with missing data. DMU uses only the information available in the dataset to prepare the statistical models. DMU segments the original dataset into small complete datasets. The study uses hierarchical clustering to segment the original dataset into small complete datasets followed by Bayesian regression on each of the small complete datasets. Predictor estimates are updated using the posterior estimates from each dataset. The performance of DMU is evaluated by using both simulated data and real studies and show better results or at par with other approaches like CCA and PMM. Conclusion DMU approach provides an alternative to the existing approaches of information elimination and imputation in processing the datasets with missing values. While the study applied the approach for continuous cross-sectional data, the approach can be applied to longitudinal, categorical and time-to-event biological data.

scholarly journals Refinement of the dynamic model building method of vibroisoled gyroscopic system

2019 ◽  
Vol 0 (37) ◽  
pp. 21-32
Author(s):  
Володимир Федорович Кришталь
Keyword(s):  
Dynamic Model ◽  
Model Building ◽  
Gyroscopic System

Psycho-Cognitive Aspects of Dynamic Model-Building in Logo: A Simple Population Evolution and Predator/Prey Program

1988 ◽  
Vol 4 (3) ◽  
pp. 227-252 ◽  
Author(s):  
Bruno Vitale
Keyword(s):  
Dynamical Systems ◽  
Dynamic Model ◽  
Model Building ◽  
Mutual Interaction ◽  
Dynamical Models ◽  
Single Population ◽  
Predator Prey ◽  
Population Evolution ◽  
Programming Skills ◽  
Two Populations

A family of simple models, which can be deployed from the case of the growth of a single population to the mutual interaction of two populations in a predators/prey relation, is programmed in LOGO by using the most elementary programming skills. The deployment is followed step by step, by emphasizing the elements of cognitive novelty and the possible cognitive obstructions, more than the possible programming difficulties. This family of models is used to model a way of introducing, through programming experience, dynamical models of change and a first approach to dynamical systems.

Statistical tools for optimal dynamic model building

2000 ◽  
Vol 24 (2-7) ◽  
pp. 1261-1267 ◽  
Author(s):  
S.P. Asprey ◽  
S. Macchietto
Keyword(s):  
Dynamic Model ◽  
Model Building ◽  
Statistical Tools ◽  
Optimal Dynamic

Static and dynamic model building explored in Baku

2020 ◽  
Vol 39 (1) ◽  
pp. 63-64
Author(s):  
Nazim Abdullayev ◽  
Peter Cook ◽  
Aleksandra Kramtseva
Keyword(s):  
Advisory Committee ◽  
Dynamic Model ◽  
Model Building ◽  
Technical Conference ◽  
Scale Modeling ◽  
Basin Scale

An SEG workshop titled, “Geophysical inputs to static and dynamic model building” was recently held in Baku, Azerbaijan. It was developed and supported by the SEG Eurasia Regional Advisory Committee. The workshop was conducted during the SPE Annual Caspian Technical Conference and was divided into three themes: integration of geophysical products, basin-scale modeling, and petrophysics.

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