论文标题
通过复发性神经网络对微生物生长进行视频框架预测
Performing Video Frame Prediction of Microbial Growth with a Recurrent Neural Network
论文作者
论文摘要
复发性神经网络（RNN）用于对铜绿假单胞菌的两个突变体的种群进行微生物生长的预测。对RNN进行了使用20帧的视频培训，这些视频是使用荧光显微镜和微流体制作的。该网络预测了每个视频的最后10帧，并且通过比较原始图像，人群曲线以及单个菌落的数量和大小来评估预测的准确性。总体而言，我们发现使用这种方法准确地预测了预测。讨论了该结果对设计微生物学的自主实验的含义，并讨论了可以使预测更加准确的步骤。
A Recurrent Neural Network (RNN) was used to perform video frame prediction of microbial growth for a population of two mutants of Pseudomonas aeruginosa. The RNN was trained on videos of 20 frames that were acquired using fluorescence microscopy and microfluidics. The network predicted the last 10 frames of each video, and the accuracy's of the predictions was assessed by comparing raw images, population curves, and the number and size of individual colonies. Overall, we found the predictions to be accurate using this approach. The implications this result has on designing autonomous experiments in microbiology, and the steps that can be taken to make the predictions even more accurate, are discussed.
