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G2Φnet: Relating genotype and biomechanical phenotype of tissues with deep learning
Fig 3
Architecture of G2Φnet.
(A) The learning stage. For each aortic sample, we input stress-stretch measurements into the branch encoder, which compresses the input into the sample feature η in the latent space. The genotype (class feature ζ) serves as additional input to the latent space. The branch encoder takes the combination of class and sample features as inputs. The trunk net takes an arbitrary stretch state (λθ, λz) as inputs. The inner product of the outputs from the branch decoder and the trunk net is the predicted stress 
at a given stretch state (λθ, λz). The trainable parameters for this stage include the weights and biases of the three sub-networks. (B) The inference stage. The branch decoder and trunk net serve together as an approximation of the stress-stretch relationship parameterized by the class feature ζ (genotype) and sample feature η. The weights and biases of the two sub-networks are fixed to be their values upon completion of the learning stage, while the class and sample features are trainable. (C) The inference stage using ensemble. With K copies of the trained model, their branch decoders (B.D.) and trunk nets (T.N.) are integrated into a single mega-model for inferring new samples. Input to the trunk net (λθ, λz) and class feature ζ are shared across model copies. The stress prediction 
is the mean value across K copies.
doi: https://doi.org/10.1371/journal.pcbi.1010660.g003