scholarly journals Experimental design schemes for learning Boolean network models

2014 ◽  
Vol 30 (17) ◽  
pp. i445-i452 ◽  
Author(s):  
N. Atias ◽  
M. Gershenzon ◽  
K. Labazin ◽  
R. Sharan
Keyword(s):  
Experimental Design ◽  
Boolean Network ◽  
Network Models

scholarly journals Towards Enhanced Performance of Neural-Network-Based Fault Detection Using an Sequential D-Optimum Experimental Design

2018 ◽  
Vol 8 (8) ◽  
pp. 1290 ◽  
Author(s):  
Beata Mrugalska
Keyword(s):  
Neural Network ◽  
Experimental Design ◽  
Fault Detection ◽  
Network Models ◽  
Neural Model ◽  
Neural Network Models ◽  
Optimum Experimental Design ◽  
The Neural Network ◽  
Adaptive Thresholds ◽  
Linear Neural Network

Increasing expectations of industrial system reliability require development of more effective and robust fault diagnosis methods. The paper presents a framework for quality improvement on the neural model applied for fault detection purposes. In particular, the proposed approach starts with an adaptation of the modified quasi-outer-bounding algorithm towards non-linear neural network models. Subsequently, its convergence is proven using quadratic boundedness paradigm. The obtained algorithm is then equipped with the sequential D-optimum experimental design mechanism allowing gradual reduction of the neural model uncertainty. Finally, an emerging robust fault detection framework on the basis of the neural network uncertainty description as the adaptive thresholds is proposed.

scholarly journals Deriving a Boolean dynamics to reveal macrophage activation with in vitro temporal cytokine expression profiles

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Ricardo Ramirez ◽  
Allen Michael Herrera ◽  
Joshua Ramirez ◽  
Chunjiang Qian ◽  
David W. Melton ◽  
...  
Keyword(s):  
Boolean Network ◽  
Macrophage Activation ◽  
Expression Profiles ◽  
Network Models ◽  
Cytokine Expression ◽  
Boolean Networks ◽  
Experimental Results ◽  
Activated Macrophages ◽  
Activation Patterns

Abstract Background Macrophages show versatile functions in innate immunity, infectious diseases, and progression of cancers and cardiovascular diseases. These versatile functions of macrophages are conducted by different macrophage phenotypes classified as classically activated macrophages and alternatively activated macrophages due to different stimuli in the complex in vivo cytokine environment. Dissecting the regulation of macrophage activations will have a significant impact on disease progression and therapeutic strategy. Mathematical modeling of macrophage activation can improve the understanding of this biological process through quantitative analysis and provide guidance to facilitate future experimental design. However, few results have been reported for a complete model of macrophage activation patterns. Results We globally searched and reviewed literature for macrophage activation from PubMed databases and screened the published experimental results. Temporal in vitro macrophage cytokine expression profiles from published results were selected to establish Boolean network models for macrophage activation patterns in response to three different stimuli. A combination of modeling methods including clustering, binarization, linear programming (LP), Boolean function determination, and semi-tensor product was applied to establish Boolean networks to quantify three macrophage activation patterns. The structure of the networks was confirmed based on protein-protein-interaction databases, pathway databases, and published experimental results. Computational predictions of the network evolution were compared against real experimental results to validate the effectiveness of the Boolean network models. Conclusion Three macrophage activation core evolution maps were established based on the Boolean networks using Matlab. Cytokine signatures of macrophage activation patterns were identified, providing a possible determination of macrophage activations using extracellular cytokine measurements.

scholarly journals OPTIMIZING INDIVIDUALS’ DAILY FUNCTION: A BOOLEAN NETWORK APPROACH FOR IDENTIFYING INTERVENTION SEQUENCES

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S376-S376
Author(s):  
Xiao Yang ◽  
Xiao Yang ◽  
Nilam Ram ◽  
David Conroy ◽  
Aaron Pincus ◽  
...  
Keyword(s):  
Boolean Network ◽  
Healthy Aging ◽  
Network Models ◽  
Developmental Systems ◽  
Intensive Longitudinal Data ◽  
Produce Change ◽  
Network Methods ◽  
Development And Aging ◽  
And Behavior ◽  
Functional Components

Abstract Development and aging are the product of a process wherein an individuals’ functional components co-act to produce change. System dynamics can be described using a variety of methods. In this paper we illustrate how Boolean network methods may be used to describe the sequences of emotion and behavior states that lead to a stable equilibrium – e.g., healthy function; and the interventions needed to push an individual toward healthier equilibria. We applied Boolean network models to intensive longitudinal data obtained from 150 participants (age 18-89 years) to describe individuals’ on-going psychosocial dynamics and identify the specific social behaviors that may be driving them toward undesirable and/or desirable equilibria (e.g., high and low negative emotions). Results are discussed with respect to how they inform theory about developmental systems, and construction of interventions meant to guide individuals toward healthy aging.

COMBINATORIAL FRACTAL GEOMETRY WITH A BIOLOGICAL APPLICATION

Fractals ◽  
2006 ◽  
Vol 14 (02) ◽  
pp. 133-142 ◽  
Author(s):  
JOHN KONVALINA ◽  
IGOR KONFISAKHAR ◽  
JACK HEIDEL ◽  
JIM ROGERS
Keyword(s):  
Fractal Geometry ◽  
Boolean Network ◽  
Biological Systems ◽  
Sierpinski Gasket ◽  
Network Models ◽  
Combinatorial Problem ◽  
Higher Dimensions ◽  
Global Dynamics ◽  
Biological Application ◽  
Sierpiński Gasket

The solution to a deceptively simple combinatorial problem on bit strings results in the emergence of a fractal related to the Sierpinski Gasket. The result is generalized to higher dimensions and applied to the study of global dynamics in Boolean network models of complex biological systems.

scholarly journals Random Boolean network models and the yeast transcriptional network

2003 ◽  
Vol 100 (25) ◽  
pp. 14796-14799 ◽  
Author(s):  
S. Kauffman ◽  
C. Peterson ◽  
B. Samuelsson ◽  
C. Troein
Keyword(s):  
Boolean Network ◽  
Network Models ◽  
Transcriptional Network
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