抗脆弱性通过卷积神经网络的多类分类预测生物网络的鲁棒性和可进化性
Antifragility Predicts the Robustness and Evolvability of Biological Networks through Multi-class Classification with a Convolutional Neural Network
AI总结：
本文提出利用抗脆弱性度量通过CNN多类分类预测生物网络的鲁棒性和可进化性。
摘要：
鲁棒性和可进化性是生物网络进化中的基本性质。为了确定一个生物网络是否鲁棒和/或可进化，需要比较其突变前后的功能。然而，随着网络规模的增长，这种比较有时会带来高昂的计算成本。在这里，我们开发了一种预测方法，以估计生物网络的鲁棒性和可进化性，而无需显式比较功能。我们通过布尔网络模型测量生物系统的抗脆弱性，并将其用作预测器。抗脆弱性发生在系统从外部扰动中受益时。通过比较原始和突变生物网络的抗脆弱性差异，我们训练了一个卷积神经网络（CNN）并测试其对鲁棒性和可进化性属性的分类。我们发现，我们的CNN模型能够成功分类这些属性。因此，我们得出结论，我们的抗脆弱性度量可以作为预测生物网络鲁棒性和可进化性的预测器。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
