Neural Network Prediction Model to Explore Complex Nonlinear Behavior in Dynamic Biological Network
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Alsharaiah, M.A. | - |
dc.contributor.author | Baniata, L.H. | - |
dc.contributor.author | Al, Adwan O. | - |
dc.contributor.author | Alghanam, O.A. | - |
dc.contributor.author | Abu-Shareha, A.A. | - |
dc.contributor.author | Alzboon, L. | - |
dc.contributor.author | Mustafa, N. | - |
dc.contributor.author | Baniata, M. | - |
dc.date.accessioned | 2022-07-23T12:40:09Z | - |
dc.date.available | 2022-07-23T12:40:09Z | - |
dc.date.created | 2022-07-23 | - |
dc.date.issued | 2022-06 | - |
dc.identifier.issn | 1865-7923 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/85126 | - |
dc.description.abstract | 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. © 2022. International Journal of Interactive Mobile Technologies. All Rights Reserved. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | International Association of Online Engineering | - |
dc.relation.isPartOf | International Journal of Interactive Mobile Technologies | - |
dc.title | Neural Network Prediction Model to Explore Complex Nonlinear Behavior in Dynamic Biological Network | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.doi | 10.3991/ijim.v16i12.30467 | - |
dc.identifier.bibliographicCitation | International Journal of Interactive Mobile Technologies, v.16, no.12, pp.32 - 51 | - |
dc.description.isOpenAccess | Y | - |
dc.identifier.scopusid | 2-s2.0-85133022963 | - |
dc.citation.endPage | 51 | - |
dc.citation.startPage | 32 | - |
dc.citation.title | International Journal of Interactive Mobile Technologies | - |
dc.citation.volume | 16 | - |
dc.citation.number | 12 | - |
dc.contributor.affiliatedAuthor | Baniata, L.H. | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | Artificial neural feed forward network | - |
dc.subject.keywordAuthor | Back propagation | - |
dc.subject.keywordAuthor | Cyclin | - |
dc.subject.keywordAuthor | Degradation | - |
dc.subject.keywordAuthor | Ordinary differential equations model (ode’s) | - |
dc.subject.keywordAuthor | Organism | - |
dc.subject.keywordAuthor | Prediction | - |
dc.subject.keywordAuthor | Progression process | - |
dc.subject.keywordAuthor | Synthesis | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
1342, Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, Republic of Korea(13120)031-750-5114
COPYRIGHT 2020 Gachon University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.