Machine Learning Techniques in Structure-Property Optimization of Polymeric Scaffolds for Tissue Engineering

9 pages•Published: March 22, 2022

Zigeng Wang, Xia Xiao, Syam Nukavarapu, Sangamesh Kumbar and Sanguthevar Rajasekaran

Abstract

Biomaterials and biomedical implants have revolutionized the way medicine is practiced. Technologies, such as 3D printing and electrospinning, are currently employed to create novel biomaterials. Most of the synthesis techniques are ad-hoc, time taking, and expensive. These shortcomings can be overcome greatly with the employment of computational techniques. In this paper we consider the problem of bone tissue engineering as an example and show the potentials of machine learning approaches in biomaterial construction, in which different models was built to predict the elastic modulus of the scaffold at given an arbitrary material composition. Likewise, the methodology was extended to cell-material interaction and prediction at an arbitrary process parameter.

Keyphrases: biomaterial, machine learning, tissue engineering

In: Hisham Al-Mubaid, Tamer Aldwairi and Oliver Eulenstein (editors). Proceedings of 14th International Conference on Bioinformatics and Computational Biology, vol 83, pages 146-154.

Links:	https://easychair.org/publications/paper/9Dfp
	https://doi.org/10.29007/nxm3

BibTeX entry

@inproceedings{BICOB2022:Machine_Learning_Techniques_Structure,
  author    = {Zigeng Wang and Xia Xiao and Syam Nukavarapu and Sangamesh Kumbar and Sanguthevar Rajasekaran},
  title     = {Machine Learning Techniques in Structure-Property Optimization of Polymeric Scaffolds for Tissue Engineering},
  booktitle = {Proceedings of 14th International Conference on Bioinformatics and Computational Biology},
  editor    = {Hisham Al-Mubaid and Tamer Aldwairi and Oliver Eulenstein},
  series    = {EPiC Series in Computing},
  volume    = {83},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/9Dfp},
  doi       = {10.29007/nxm3},
  pages     = {146-154},
  year      = {2022}}

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