G2Φnet: Relating genotype and biomechanical phenotype of tissues with deep learning.

ABSTRACT: Many genetic mutations adversely affect the structure and function of load-bearing soft tissues, with clinical sequelae often responsible for disability or death. Parallel advances in genetics and histomechanical characterization provide significant insight into these conditions, but there remains a pressing need to integrate such information. We present a novel genotype-to-biomechanical phenotype neural network (G2Φnet) for characterizing and classifying biomechanical properties of soft tissues, which serve as important functional readouts of tissue health or disease. We illustrate the utility of our approach by inferring the nonlinear, genotype-dependent constitutive behavior of the aorta for four mouse models involving defects or deficiencies in extracellular constituents. We show that

SUBMITTER: Zhang E 

PROVIDER: S-EPMC9668200 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature