Neural Network Prediction Model to Explore Complex Nonlinear Behavior in Dynamic Biological Network

Neural Network Prediction Model to Explore Complex Nonlinear Behavior in Dynamic Biological Network

artificial neural Feed Forward Network

Back Propagation; organism

Cyclin E;

Progression process

synthesis

prediction model

Ordinary differential equations model(ODE's)

Organism network systems provide a biological data with high complex level. Besides, these data reflect the complex activities in organisms that identifies nonlinear behavior as well. Hence, mathematical modelling methods such as Ordinary Differential Equations model (ODE's) are becoming significant tools to predict, and expose implied knowledge and data. Unfortunately, the aforementioned approaches face some of cons such as the scarcity and the vagueness in the biological knowledge to expect the protein concentrations measurements. So, the main object of this research presents a computational model such as a neural Feed Forward Network model using Back Propagation algorithm to engage with imprecise and missing biological knowledge to provide more insight about biological systems in organisms. Therefore, the model predicts protein concentration and illustrates the nonlinear behavior for the biological dynamic behavior in precise form. Also, the desired results are matched with recent ODE's model and it provides precise results in simpler form than ODEs.

Alsharaiah, Mohammad A.

H. Baniata , Laith

Al Adwan , Omar

Abu Alghanam, Orieb

Abu-Shareha, Ahmad Adel

Alzboon , Laith

Mustafa, Nedal

Baniata , Mohammad

International Journal of Interactive Mobile Technologies (iJIM); Vol. 16 No. 12 (2022); pp. 32-51

1865-7923

International Association of Online Engineering (IAOE), Vienna, Austria

2022-06-21

Copyright (c) 2022 mohammad alsharaiah, Laith H. Baniata , Omar Al Adwan , Orieb Abu Alghanam, Ahmad Abo Shareih, Laith Alzboon , Mohammad Baniata

https://creativecommons.org/licenses/by/4.0

application/pdf

eng

info:eu-repo/semantics/article

info:eu-repo/semantics/publishedVersion

Peer-reviewed Article