首页|A deep-reinforcement learning approach for optimizing homogeneous droplet routing in digital microfluidic biochips
A deep-reinforcement learning approach for optimizing homogeneous droplet routing in digital microfluidic biochips
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维普
Over the past two decades,digital microfluidic biochips have been in much demand for safety-critical and biomedical applications and increas-ingly 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 expe-riences and updates the routing paths.The proposed algorithm was applied to benchmark suites Ⅰ and Ⅲ as two different test benches,and it achieved significant improvements over state-of-the-art techniques.
Digital microfluidicsBiochipDroplet routingFluidic constraintsDeep learningReinforcement learning
Basudev Saha、Bidyut Das、Mukta Majumder
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Department of Computer Science and Technology,University of North Bengal,Darjeeling 734013,India
Department of Information Technology,Haldia Institute of Technology,Haldia 721657,India
NETL
NSTL
万方数据
维普
