Time series analysis of nucleic acid reactions via a generalized transformer model

The contemporary landscape of medical diagnostics and therapeutic interventions has witnessed a remarkable surge in the production of time series data. Artificial intelligence (AI), particularly the deep learning, has presented promising values in investigating the high-dimension and meaningful significance hidden behind these diagnostic data. In this work, we propose a novel analytics for intelligent nucleic acid amplification tests (NAAT) based on deep learning and paper microfluidics. On-chip amplification data were straightforwardly fed to a deep learning model derived from Transformer neural network. To facilitate the development and deployment of the approach, we conducted a lightweight processing of the Transformer model. Then, the capacity of the model for accurately predicting the reaction trend and end-point value was validated. We also employed ablation experiments to evaluate the effects of various parameters on prediction performance followed by optimizing the model. Then, three clinical datasets including 706 positive and 205 negative samples obtained from Fujian Provincial Hospital were used to verify the generalization of the approach. Without any modification of the model structure and hyperparameters, accuracy, sensitivity, and specificity by the presented approach were 98.28 %, 97.52 % and 99.02 %. Further comparison studies based on the nine different AI algorithms including recurrent neural network and long-short term memory were performed. The presented study holds potential to facilitating routine diagnostic tasks for preventing pandemic and propelling the development of smart portable instruments.