首页> International journal of biological macromolecules> Data-driven supervised machine learning to predict the compressive response of porous PVA/Gelatin hydrogels and in-vitro assessments: Employing design of experiments.

Data-driven supervised machine learning to predict the compressive response of porous PVA/Gelatin hydrogels and in-vitro assessments: Employing design of experiments.

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第一作者： Ali,Khalvandi

第一单位： Composites Research Laboratory (CRLab), Amirkabir University of Technology, Tehran, Iran; New Technologies Research Center, Amirkabir University of Technology, Tehran, Iran; Department of Mechanical Engineering, Amirkabir University of Technology, Tehran, Iran. Electronic address: alikhalvandi@aut.ac.ir.

作者： Ali,Khalvandi ^[1] ; Lobat,Tayebi ^[2] ; Saeed,Kamarian ^[3] ; Saeed,Saber-Samandari ^[4] ; Jung-Il,Song ^[3]

作者单位： Composites Research Laboratory (CRLab), Amirkabir University of Technology, Tehran, Iran; New Technologies Research Center, Amirkabir University of Technology, Tehran, Iran; Department of Mechanical Engineering, Amirkabir University of Technology, Tehran, Iran. Electronic address: alikhalvandi@aut.ac.ir. ^[1] School of Dentistry, Marquette University, Milwaukee, WI 53233, United States. ^[2] Mechanical Engineering Department, Changwon National University, Changwon, Republic of Korea. ^[3] Composites Research Laboratory (CRLab), Amirkabir University of Technology, Tehran, Iran; New Technologies Research Center, Amirkabir University of Technology, Tehran, Iran. Electronic address: saeedss@aut.ac.ir. ^[4]

关键词 Biocompatible hydrogels Cell adhesion Deep neural networks Mechanical properties Microstructural properties Porous materials

医学主题词小鼠(Mice);动物(Animals);明胶(Gelatin);多孔性(Porosity);生物相容性材料(Biocompatible Materials);聚乙烯乙醇(Polyvinyl Alcohol);水凝胶类(Hydrogels)

DOI 10.1016/j.ijbiomac.2023.126906

PMID 37716655

发布时间 2023-11-24

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准确一般字义偏差语意偏离译文作用小提交