  Robustness and evolvability are essential properties to the evolution of
biological networks. To determine if a biological network is robust and/or
evolvable, it is required to compare its functions before and after mutations.
However, this sometimes takes a high computational cost as the network size
grows. Here we develop a predictive method to estimate the robustness and
evolvability of biological networks without an explicit comparison of
functions. We measure antifragility in Boolean network models of biological
systems and use this as the predictor. Antifragility occurs when a system
benefits from external perturbations. By means of the differences of
antifragility between the original and mutated biological networks, we train a
convolutional neural network (CNN) and test it to classify the properties of
robustness and evolvability. We found that our CNN model successfully
classified the properties. Thus, we conclude that our antifragility measure can
be used as a predictor of the robustness and evolvability of biological
networks.
