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An explainable machine learning-based probabilistic framework for the design of scaffolds in bone tissue engineering.

Drakoulas, G ; Gortsas, T ; et al.
In: Biomechanics and modeling in mechanobiology, Jg. 23 (2024-06-01), Heft 3, S. 987-1012
Online academicJournal

Titel:
An explainable machine learning-based probabilistic framework for the design of scaffolds in bone tissue engineering.
Autor/in / Beteiligte Person: Drakoulas, G ; Gortsas, T ; Polyzos, E ; Tsinopoulos, S ; Pyl, L ; Polyzos, D
Link:
Zeitschrift: Biomechanics and modeling in mechanobiology, Jg. 23 (2024-06-01), Heft 3, S. 987-1012
Veröffentlichung: Berlin ; New York : Springer, c2002-, 2024
Medientyp: academicJournal
ISSN: 1617-7940 (electronic)
DOI: 10.1007/s10237-024-01817-7
Schlagwort:
  • Probability
  • Stress, Mechanical
  • Humans
  • Computer Simulation
  • Polyesters
  • Tissue Scaffolds chemistry
  • Tissue Engineering methods
  • Machine Learning
  • Bone and Bones physiology
Sonstiges:
  • Nachgewiesen in: MEDLINE
  • Sprachen: English
  • Publication Type: Journal Article
  • Language: English
  • [Biomech Model Mechanobiol] 2024 Jun; Vol. 23 (3), pp. 987-1012. <i>Date of Electronic Publication: </i>2024 Feb 28.
  • MeSH Terms: Tissue Scaffolds* / chemistry ; Tissue Engineering* / methods ; Machine Learning* ; Bone and Bones* / physiology ; Probability ; Stress, Mechanical ; Humans ; Computer Simulation ; Polyesters
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  • Grant Information: Project Number: 2060 Hellenic Foundation for Research and Innovation
  • Contributed Indexing: Keywords: Bone scaffolds; Computational biomechanics; Explainable artificial intelligence; Machine learning; Multiobjective optimization; Reduced-order model
  • Substance Nomenclature: 0 (poly(lactide))
  • Entry Date(s): Date Created: 20240228 Date Completed: 20240517 Latest Revision: 20240517
  • Update Code: 20240517

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