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Zhuang, et al.
yield stress, viscoelasticity, and shape fidelity from using cells are likely to provide deeper insights into the stage-
various type I collagen-based bio-inks [190] . By separating dependent, patient-specific tumor cell behavior, further
the class variables into shape fidelity and extrusion, elucidating tumor progression dynamics, and thus
the machine learning algorithm effectively optimized facilitating stronger anticancer therapeutic development.
the composite bio-ink material fraction and subsequent
printing performance [191,192] . Current applications of Acknowledgments
3D bioprinting based machine learning algorithms are The authors would like to thank funding support by NIH
currently geared towards using regressive models such NIGMS MIRA award 1R35GM133794 to Dr. Mei He
as LASSO; however, a potential avenue of integrating
advanced learning systems using generative ensembles Conflicts of interest
or Bayesian approaches in producing highest performing
inks of spheroidal assembly remains completely untapped. The authors declare no conflict of interest.
Current existing technologies are challenged by spheroid
precision positioning coupled with an assembly process References
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