Analysis of trabecular bone mechanics using machine learning

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dc.contributor.authorSohail, Ayesha
dc.contributor.authorYounas, Muhammad
dc.contributor.authorBhatti, Yousaf
dc.contributor.authorLi, Zhiwu
dc.contributor.authorTunç, Sümeyye
dc.contributor.authorAbid, Muhammad
dc.date.accessioned10.07.201910:49:13
dc.date.accessioned2019-07-10T19:49:38Z
dc.date.available10.07.201910:49:13
dc.date.available2019-07-10T19:49:38Z
dc.date.issued2019
dc.departmentİstanbul Medipol Üniversitesi, İMÜ Meslek Yüksekokulu, Fizyoterapi Ana Bilim Dalı
dc.descriptionWOS: 000464252300001
dc.descriptionPubMed ID: 30936677
dc.description.abstractBone 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.
dc.description.sponsorshipScience Technology Development Fund, MSAR [078/2015/A3, 122/2017/A3]en_US
dc.description.sponsorshipThe 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.identifier.citationSohail, 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
dc.identifier.doi10.1177/1176934318825084
dc.identifier.issn1176-9343
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://dx.doi.org/10.1177/1176934318825084
dc.identifier.urihttps://hdl.handle.net/20.500.12511/1694
dc.identifier.volume15
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherSage Publications Ltd
dc.relation.ispartofEvolutionary Bioinformaticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://www.creativecommons.org/licenses/by-nc/4.0/*
dc.subjectFerrofluids
dc.subjectNanomedicine
dc.subjectDrug Targeting
dc.subjectHyperthermia
dc.titleAnalysis of trabecular bone mechanics using machine learning
dc.typeArticle

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