NNVA: Neural Network Assisted Visual Analysis of Yeast Cell Polarization Simulation
NNVA: Neural Network Assisted Visual Analysis of Yeast Cell Polarization Simulation
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DOI：
10.1109/tvcg.2019.2934591
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发表时间：
2020-01-01
影响因子：
5.2
通讯作者：
Chou, Ching-Shan
中科院分区：
文献类型：
--
作者：
Hazarika, Subhashis;Li, Haoyu;Chou, Ching-Shan
复杂的计算模型通常被设计用于模拟许多科学学科中的真实世界物理现象。然而，这些模拟模型往往在计算上非常昂贵，并且涉及大量的模拟输入参数，在模型可以应用于真实的科学研究之前，需要对这些参数进行分析和适当校准。我们提出了一个可视化的分析系统，以方便交互式的探索性分析高维输入参数空间的复杂酵母细胞极化模拟。所提出的系统可以帮助设计仿真模型的计算生物学家通过修改参数值并立即可视化预测的仿真结果来可视化地校准输入参数，而无需为每个实例运行原始昂贵的仿真。我们提出的视觉分析系统是由一个训练的基于神经网络的代理模型作为后端分析框架。在这项工作中，我们证明了使用神经网络作为替代模型的视觉分析的优势，结合一些最新进展领域的不确定性量化，可解释性和基于神经网络的模型的解释。我们利用训练好的网络进行交互式的参数敏感性分析的原始模拟，以及建议最佳的参数配置，使用激活最大化框架的神经网络。我们还促进了对训练网络的详细分析，以提取有关网络在训练过程中学习的仿真模型的有用见解。我们进行了两个案例研究，并发现了多个新的参数配置，可以触发高细胞极化的结果，在原来的模拟模型。我们通过与原始模拟模型结果以及我们的专家进行的先前参数分析的结果进行比较来评估我们的结果。
Complex computational models are often designed to simulate real-world physical phenomena in many scientific disciplines. However, these simulation models tend to be computationally very expensive and involve a large number of simulation input parameters, which need to be analyzed and properly calibrated before the models can be applied for real scientific studies. We propose a visual analysis system to facilitate interactive exploratory analysis of high-dimensional input parameter space for a complex yeast cell polarization simulation. The proposed system can assist the computational biologists, who designed the simulation model, to visually calibrate the input parameters by modifying the parameter values and immediately visualizing the predicted simulation outcome without having the need to run the original expensive simulation for every instance. Our proposed visual analysis system is driven by a trained neural network-based surrogate model as the backend analysis framework. In this work, we demonstrate the advantage of using neural networks as surrogate models for visual analysis by incorporating some of the recent advances in the field of uncertainty quantification, interpretability and explainability of neural network-based models. We utilize the trained network to perform interactive parameter sensitivity analysis of the original simulation as well as recommend optimal parameter configurations using the activation maximization framework of neural networks. We also facilitate detail analysis of the trained network to extract useful insights about the simulation model, learned by the network, during the training process. We performed two case studies, and discovered multiple new parameter configurations, which can trigger high cell polarization results in the original simulation model. We evaluated our results by comparing with the original simulation model outcomes as well as the findings from previous parameter analysis performed by our experts.