用物理约束神经网络，从稀疏数据中推演膀胱癌免疫治疗动态。
Modeling Adoptive Cell Therapy in Bladder Cancer from Sparse Biological Data using PINNs
- 将微分方程规律嵌入神经网络损失函数，引导模型学习治疗过程
- 仅用少量肿瘤体积数据即实现对参数时变性的准确建模
- 适合缺乏连续观测的临床研究，尤其适用于组合疗法分析
物理信息神经网络（PINNs）通过在损失函数中嵌入由微分方程描述的动力系统规律，实现对动态系统的建模。本文首次将该方法应用于肿瘤学领域，旨在从稀疏实验数据中学习联合治疗下肿瘤微环境中的时变相互作用。由于临床数据通常仅有少数时间点的肿瘤体积测量值，我们通过引入基于先验知识的归纳偏置，并将观测到的生物学约束作为正则化项，扩展了传统PINN框架。改进后的算法在仅有少量训练样本的情况下仍能收敛至合理解，并具备良好泛化能力。我们在一个用于间歇性联合治疗的常微分方程（ODE）模型上验证了该方法的有效性，成功求解出对应ODE的动态解及部分参数的时变形式。通过均方误差（MSE）、平均绝对误差（MAE）和平均绝对百分比误差（MAPE）等指标评估，结果表现出强收敛性。
原文摘要 · Abstract (English)
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).
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。