论文标题
通过卷积神经网络通过多级分类来预测生物网络的鲁棒性和发展性
Antifragility Predicts the Robustness and Evolvability of Biological Networks through Multi-class Classification with a Convolutional Neural Network
论文作者
论文摘要
鲁棒性和进化性是生物网络发展的重要特性。为了确定生物网络是否可靠和/或可发展，需要比较突变之前和之后其功能。但是，随着网络尺寸的增长，这有时需要高计算成本。在这里，我们开发了一种预测方法来估计生物网络的鲁棒性和发展性，而无需明确的功能比较。我们测量生物系统布尔网络模型中的抗差异，并将其用作预测因子。当系统受益于外部扰动时，就会发生抗差异。通过原始生物网络和突变的生物网络之间的抗差异差异，我们训练卷积神经网络（CNN），并测试它以对鲁棒性和发展性的特性进行分类。我们发现我们的CNN模型成功地对属性进行了分类。因此，我们得出的结论是，我们的抗差异度量可以用作生物网络鲁棒性和可发展性的预测指标。
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.
