dc.contributor.author | Sohail, Ayesha | |
dc.contributor.author | Younas, Muhammad | |
dc.contributor.author | Bhatti, Yousaf | |
dc.contributor.author | Li, Zhiwu | |
dc.contributor.author | Tunç, Sümeyye | |
dc.contributor.author | Abid, Muhammad | |
dc.date.accessioned | 10.07.201910:49:13 | |
dc.date.accessioned | 2019-07-10T19:49:38Z | |
dc.date.available | 10.07.201910:49:13 | |
dc.date.available | 2019-07-10T19:49:38Z | |
dc.date.issued | 2019 | en_US |
dc.identifier.citation | Sohail, A., Younas, M., Bhatti, Y., Li, Z., Tunç, S. ve Abid, M. (2019). Analysis of trabecular bone mechanics using machine learning. Evolutionary Bioinformatics, 15. https://dx.doi.org/10.1177/1176934318825084 | en_US |
dc.identifier.issn | 1176-9343 | |
dc.identifier.uri | https://dx.doi.org/10.1177/1176934318825084 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12511/1694 | |
dc.description | WOS: 000464252300001 | en_US |
dc.description | PubMed ID: 30936677 | en_US |
dc.description.abstract | Bone remodeling is a dynamic process, and mutliphase analysis incorporated with the forecasting algorithm can help the biologists and orthopedics to interpret the laboratory generated results and to apply them in improving applications in the fields of "drug design, treatment, and therapy" of diseased bones. The metastasized bone microenvironment has always remained a challenging puzzle for the researchers. A multiphase computational model is interfaced with the artificial intelligence algorithm in a hybrid manner during this research. Trabecular surface remodeling is presented in this article, with the aid of video graphic footage, and the associated parametric thresholds are derived from artificial intelligence and clinical data. | en_US |
dc.description.sponsorship | Science Technology Development Fund, MSAR [078/2015/A3, 122/2017/A3] | en_US |
dc.description.sponsorship | The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported in part by the Science Technology Development Fund, MSAR, under grant nos 078/2015/A3 and 122/2017/A3. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Sage Publications Ltd | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.rights | Attribution-NonCommercial 4.0 International | * |
dc.rights.uri | https://www.creativecommons.org/licenses/by-nc/4.0/ | * |
dc.subject | Ferrofluids | en_US |
dc.subject | Nanomedicine | en_US |
dc.subject | Drug Targeting | en_US |
dc.subject | Hyperthermia | en_US |
dc.title | Analysis of trabecular bone mechanics using machine learning | en_US |
dc.type | article | en_US |
dc.relation.ispartof | Evolutionary Bioinformatics | en_US |
dc.department | İstanbul Medipol Üniversitesi, İMÜ Meslek Yüksekokulu, Fizyoterapi Ana Bilim Dalı | en_US |
dc.identifier.volume | 15 | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.identifier.doi | 10.1177/1176934318825084 | en_US |
dc.identifier.wosquality | Q4 | en_US |
dc.identifier.scopusquality | Q2 | en_US |