用神经网络预测细胞迁移与分裂，融合机械力与环境因素。
Augmented Structure Preserving Neural Networks for cell biomechanics
- 结合结构保持网络与深度学习，模拟细胞集体运动
- 轨迹预测准确率高，可捕捉真实迁移路径
- 适合生物力学、肿瘤生长研究者参考
细胞生物力学涉及生命演化及胚胎发生、损伤修复和肿瘤生长等众多复杂过程。尽管研究日益深入，但细胞间相互作用及其对群体决策的影响仍不明确。本文提出一种新方法：将结构保持神经网络（Structure Preserving Neural Networks）与人工神经网络结合，前者建模细胞纯机械运动，后者通过计算机视觉提取实验中的环境因素。该模型在模拟和真实细胞迁移数据上均表现良好，采用滚动预测策略，能高精度预测完整细胞轨迹。此外，还构建了基于相同特征的有丝分裂事件预测模型。
原文摘要 · Abstract (English)
Cell biomechanics involve a great number of complex phenomena that are fundamental to the evolution of life itself and other associated processes, ranging from the very early stages of embryo-genesis to the maintenance of damaged structures or the growth of tumors. Given the importance of such phenomena, increasing research has been dedicated to their understanding, but the many interactions between them and their influence on the decisions of cells as a collective network or cluster remain unclear. We present a new approach that combines Structure Preserving Neural Networks, which study cell movements as a purely mechanical system, with other Machine Learning tools (Artificial Neural Networks), which allow taking into consideration environmental factors that can be directly deduced from an experiment with Computer Vision techniques. This new model, tested on simulated and real cell migration cases, predicts complete cell trajectories following a roll-out policy with a high level of accuracy. This work also includes a mitosis event prediction model based on Neural Networks architectures which makes use of the same observed features.
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