基于深度神经网络的HIV酶动力学微分方程求解器
Deep Neural Network Based Differential Equation Solver for HIV Enzyme Kinetics
摘要：
目的：我们试图使用神经网络（NN）来解决一个众所周知的微分方程系统描述T细胞和HIV病毒负荷之间的平衡。材料与方法：在本文中，我们采用一个3输入并行神经网络的一阶常微分方程描述上述生化关系的系统的近似解。结果：神经网络得到的数值结果是非常相似的主机从文献中的数值近似。结论：我们已经证明了使用NN集成的一个著名的和医学上重要的系统的一阶耦合常微分方程。我们的试错方法抵消了系统固有的规模不平衡。然而，它强调需要在今后的工作中更实质性地解决规模不平衡问题。这样做将允许更大的方程系统的自动化解决方案，这可以描述越来越复杂和生物学上有趣的系统。
英文摘要：
Purpose: We seek to use neural networks (NNs) to solve a well-known system of differential equations describing the balance between T cells and HIV viral burden. Materials and Methods: In this paper, we employ a 3-input parallel NN to approximate solutions for the system of first-order ordinary differential equations describing the above biochemical relationship. Results: The numerical results obtained by the NN are very similar to a host of numerical approximations from the literature. Conclusion: We have demonstrated use of NN integration of a well-known and medically important system of first order coupled ordinary differential equations. Our trial-and-error approach counteracts the system's inherent scale imbalance. However, it highlights the need to address scale imbalance more substantively in future work. Doing so will allow more automated solutions to larger systems of equations, which could describe increasingly complex and biologically interesting systems.
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。
