MTvis: Understanding and Optimizing Microbial Time-series Data Augmentation Model via Interactive Visualization

Microbial time-series data usually need to be obtained by professionals through biological experiments, and the amount of data obtained from such manual experiments is very limited. Deep learning generative models leverage the superior learning capabilities of neural networks to generate high-quality synthetic data, which provides a promising approach to address the above shortcomings. However, the black-box nature of deep learning models makes it difficult for domain experts to trust the generated data, limiting the application of these techniques. Thus, we propose MTvis, an interactive visualization system designed to help experts understand and optimize data generated by MT-GAN, which is a microbial time-series data augmentation model proposed by us. MT-GAN not only introduces the temperature lag effect but also captures the dynamics of microbial time-series data via adversarial and joint learning. MTvis provides two exploration modes which enable users to gain insights into the structure of the model and adjust hyperparameters dynamically. The system also provides real-time observation of distribution differences between generated and real data. Experimental results show that MT-GAN effectively improves the fidelity of the generated data, while MTvis enhances the credibility and practicality of synthetic data for domain experts.