Mapping biomaterial complexity by machine learning
Tissue Engineering Part A, 30(19-20), 662-680
This study presents a novel machine learning approach for mapping the complexity of biomaterials, enabling predictive modeling of material properties and biological responses. Our methodology demonstrates significant improvements in understanding structure-function relationships in complex biomaterial systems.
We developed computational frameworks that integrate high-dimensional material characterization data with biological outcome measurements, providing insights that accelerate the design-build-test-learn cycle in biomaterials research.