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


  • Mohammad A. Alsharaiah Al-Ahliyya Amman University (AAU)
  • Laith H. Baniata Kyungpook National University
  • Omar Al Adwan Al-Ahliyya Amman University
  • Orieb Abu Alghanam Al-Ahliyya Amman University
  • Ahmad Adel Abu-Shareha Al-Ahliyya Amman University
  • Laith Alzboon Al-Ahliyya Amman University
  • Nedal Mustafa Al-Ahliyya Amman University
  • Mohammad Baniata Ubion



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.




How to Cite

Alsharaiah, M. A., H. Baniata , L. ., Al Adwan , O. ., Abu Alghanam, O. ., Abu-Shareha, A. A. ., Alzboon , L. ., Mustafa, N. ., & Baniata , M. (2022). Neural Network Prediction Model to Explore Complex Nonlinear Behavior in Dynamic Biological Network . International Journal of Interactive Mobile Technologies (iJIM), 16(12), pp. 32–51.