标题： 基于稀疏生物数据的膀胱癌适应性细胞治疗建模使用PINNs 显示英文标题

标题： Modeling Adoptive Cell Therapy in Bladder Cancer from Sparse Biological Data using PINNs

摘要： 物理信息神经网络（PINNs）是将由微分方程建模的动力系统定律嵌入其损失函数作为约束条件的神经网络。 在本工作中，我们提出了一种应用于肿瘤学的PINN框架。 在此，我们旨在学习由于组合疗法在肿瘤微环境中产生的时变相互作用。 在肿瘤学中，实验数据通常稀疏，并由少数时间点的肿瘤体积组成。 通过嵌入来自动力系统先验信息的归纳偏差，我们扩展了物理信息神经网络（PINN），并引入观察到的生物约束作为正则化代理。 修改后的PINN算法能够引导自己找到合理的解决方案，并且仅使用少量训练示例就能很好地泛化。 我们通过学习组合疗法的常微分方程（ODE）模型中间歇性治疗的动力学来展示我们方法的优势。 该算法给出了ODE的解以及ODE模型某些参数的时变形式。 我们使用均方误差（MSE）、平均绝对误差（MAE）和平均绝对百分比误差（MAPE）等指标展示了强大的收敛性。 显示英文摘要

摘要： Physics-informed neural networks (PINNs) are neural networks that embed the laws of dynamical systems modeled by differential equations into their loss function as constraints. In this work, we present a PINN framework applied to oncology. Here, we seek to learn time-varying interactions due to a combination therapy in a tumor microenvironment. In oncology, experimental data are often sparse and composed of a few time points of tumor volume. By embedding inductive biases derived from prior information about a dynamical system, we extend the physics-informed neural networks (PINN) and incorporate observed biological constraints as regularization agents. The modified PINN algorithm is able to steer itself to a reasonable solution and can generalize well with only a few training examples. We demonstrate the merit of our approach by learning the dynamics of treatment applied intermittently in an ordinary differential equation (ODE) model of a combination therapy. The algorithm yields a solution to the ODE and time-varying forms of some of the ODE model parameters. We demonstrate a strong convergence using metrics such as the mean squared error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE).