Predicting degradation rate of genipin cross-linked gelatin scaffolds with machine learning.

Entekhabi, Elahe; Haghbin Nazarpak, Masoumeh; Sedighi, Mehdi; Kazemzadeh, Arghavan

Entekhabi, Elahe; Haghbin Nazarpak, Masoumeh; Sedighi, Mehdi; Kazemzadeh, Arghavan.

Afiliación

Entekhabi E; Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran.
Haghbin Nazarpak M; New Technologies Research Center (NTRC), Amirkabir University of Technology, Tehran, Iran. Electronic address: haghbin@aut.ac.ir.
Sedighi M; New Technologies Research Center (NTRC), Amirkabir University of Technology, Tehran, Iran; Department of Mechanical Engineering, University of Sistan and Baluchestan, Zahedan, Iran.
Kazemzadeh A; School of Biology, College of Science, University of Tehran, Tehran, Iran.

Mater Sci Eng C Mater Biol Appl ; 107: 110362, 2020 Feb.

Article en En | MEDLINE | ID: mdl-31761181

Asunto(s)

Gelatina/química; Iridoides/química; Aprendizaje Automático; Andamios del Tejido/química; Reactivos de Enlaces Cruzados/química; Gelatina/metabolismo; Ensayo de Materiales; Microscopía Electrónica de Rastreo; Modelos Teóricos; Redes Neurales de la Computación; Análisis de Regresión

Palabras clave

Degradation rate; Engineering scaffolds; Prediction accuracy; Tissue engineering

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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Iridoides / Andamios del Tejido / Aprendizaje Automático / Gelatina Tipo de estudio: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Mater Sci Eng C Mater Biol Appl Año: 2020 Tipo del documento: Article País de afiliación: Irán

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