Over the past two decades,digital microfluidic biochips have been in much demand for safety-critical and biomedical applications and increasingly important in point-of-care analysis,drug discovery,and immunoassays,amo...Over the past two decades,digital microfluidic biochips have been in much demand for safety-critical and biomedical applications and increasingly important in point-of-care analysis,drug discovery,and immunoassays,among other areas.However,for complex bioassays,finding routes for the transportation of droplets in an electrowetting-on-dielectric digital biochip while maintaining their discreteness is a challenging task.In this study,we propose a deep reinforcement learning-based droplet routing technique for digital microfluidic biochips.The technique is implemented on a distributed architecture to optimize the possible paths for predefined source–target pairs of droplets.The actors of the technique calculate the possible routes of the source–target pairs and store the experience in a replay buffer,and the learner fetches the experiences and updates the routing paths.The proposed algorithm was applied to benchmark suitesⅠand Ⅲ as two different test benches,and it achieved significant improvements over state-of-the-art techniques.展开更多
文摘Over the past two decades,digital microfluidic biochips have been in much demand for safety-critical and biomedical applications and increasingly important in point-of-care analysis,drug discovery,and immunoassays,among other areas.However,for complex bioassays,finding routes for the transportation of droplets in an electrowetting-on-dielectric digital biochip while maintaining their discreteness is a challenging task.In this study,we propose a deep reinforcement learning-based droplet routing technique for digital microfluidic biochips.The technique is implemented on a distributed architecture to optimize the possible paths for predefined source–target pairs of droplets.The actors of the technique calculate the possible routes of the source–target pairs and store the experience in a replay buffer,and the learner fetches the experiences and updates the routing paths.The proposed algorithm was applied to benchmark suitesⅠand Ⅲ as two different test benches,and it achieved significant improvements over state-of-the-art techniques.
